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Kuru, Merve, and Gulben Calis. "Application of time series models for heating degree day forecasting." Organization, Technology and Management in Construction: an International Journal 12, no. 1 (2020): 2137–46. http://dx.doi.org/10.2478/otmcj-2020-0009.
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AbstractThis study aims at constructing short-term forecast models by analyzing the patterns of the heating degree day (HDD). In this context, two different time series analyses, namely the decomposition and Box–Jenkins methods, were conducted. The monthly HDD data in France between 1974 and 2017 were used for analyses. The multiplicative model and 79 SARIMA models were constructed by the decomposition and Box–Jenkins method, respectively. The performance of the SARIMA models was assessed by the adjusted R2 value, residual sum of squares, the Akaike Information Criteria, the Schwarz Information Criteria, and the analysis of the residuals. Moreover, the mean absolute percentage error, mean absolute deviation, and mean squared deviation values were calculated to evaluate the performance of both methods. The results show that the decomposition method yields more acceptable forecasts than the Box–Jenkins method for supporting short-term forecasting of the HDD.
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Sriyotha, Sasiwimon, Rojanee Homchalee, and Weerapat Sessomboon. "Forecasting of Production and Consumption of Ethanol in Thailand Using Time Series Analysis." Applied Mechanics and Materials 781 (August 2015): 651–54. http://dx.doi.org/10.4028/www.scientific.net/amm.781.651.
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Ethanol is the important renewable energy in Thailand. It is alcohol produced from sugarcane and tapioca that are agricultural products available in Thailand. Ethanol is used to blend with gasoline for use as gasohol. Ethanol production and consumption in Thailand are fluctuating. Consequently, planning of ethanol production and consumption is irrelevant. In order to solve this problem, this study aims to find forecasting models using time series analysis including exponential smoothing and the Box-Jenkins methods. The most appropriate forecasting model was selected from the two methods by considering the minimum of the mean absolute percentage error: MAPE. It was found that the Box-Jenkins is the most appropriate method to forecast both ethanol production and consumption. The forecasting results were then used to determine appropriate quantity and proportion of molasses and tapioca needed for ethanol production in the future.
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Tsoku, Johannes Tshepiso, Nonofo Phukuntsi, and Lebotsa Daniel Metsileng. "Gold sales forecasting: The Box-Jenkins methodology." Risk Governance and Control: Financial Markets and Institutions 7, no. 1 (2017): 54–60. http://dx.doi.org/10.22495/rgcv7i1art7.
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The study employs the Box-Jenkins Methodology to forecast South African gold sales. For a resource economy like South Africa where metals and minerals account for a high proportion of GDP and export earnings, the decline in gold sales is very disturbing. Box-Jenkins time series technique was used to perform time series analysis of monthly gold sales for the period January 2000 to June 2013 with the following steps: model identification, model estimation, diagnostic checking and forecasting. Furthermore, the prediction accuracy is tested using mean absolute percentage error (MAPE). From the analysis, a seasonal ARIMA(4,1,4)×(0,1,1)12 was found to be the “best fit model” with an MAPE value of 11% indicating that the model is fit to be used to predict or forecast future gold sales for South Africa. In addition, the forecast values show that there will be a decrease in the overall gold sales for the first six months of 2014. It is hoped that the study will help the public and private sectors to understand the gold sales or output scenario and later plan the gold mining activities in South Africa. Furthermore, it is hoped that this research paper has demonstrated the significance of Box-Jenkins technique for this area of research and that they will be applied in the future.
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Purnawansyah, Purnawansyah, Haviluddin Haviluddin, Rayner Alfred, and Achmad Fanany Onnilita Gaffar. "Network Traffic Time Series Performance Analysis Using Statistical Methods." Knowledge Engineering and Data Science 1, no. 1 (2017): 1. http://dx.doi.org/10.17977/um018v1i12018p1-7.
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This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting.
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Chen, Yin Ping, Ai Ping Wu, Cui Ling Wang, Hai Ying Zhou, and Shu Xiu Feng. "Time Series Analysis of Pulmonary Tuberculosis Incidence: Forecasting by Applying the Time Series Model." Advanced Materials Research 709 (June 2013): 819–22. http://dx.doi.org/10.4028/www.scientific.net/amr.709.819.
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The main objective of this study is to identify the stochastic autoregressive integrated moving average (ARIMA) model to predict the pulmonary tuberculosis incidence in Qianan. Considering the Box-Jenkins modeling approach, the incidence of pulmonary tuberculosis was collected monthly from 2004 to 2010. The model ARIMA(0,1,1)12 was established finally and the residual sequence was a white noise sequence. Then, this model was used for calculating dengue incidence for the last 6 observations compared with observed data, and performed to predict the monthly incidence in 2011. It is necessary and practical to apply the approach of ARIMA model in fitting time series to predict pulmonary tuberculosis within a short lead time.
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Menezes, Moises Lima de, Reinaldo Castro Souza, and José Francisco Moreira Pessanha. "Electricity consumption forecasting using singular spectrum analysis." DYNA 82, no. 190 (2015): 138–46. http://dx.doi.org/10.15446/dyna.v82n190.43652.
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Singular Spectrum Analysis (SSA) is a non-parametric technique that allows the decomposition of a time series into signal and noise. Thus, it is a useful technique to trend extraction, smooth and filter a time series. The effect on performance of both Box and Jenkins' and Holt-Winters models when applied to the time series filtered by SSA is investigated in this paper. Three different methodologies are evaluated in the SSA approach: Principal Component Analysis (PCA), Cluster Analysis and Graphical Analysis of Singular Vectors. In order to illustrate and compare the methodologies, in this paper, we also present the main results of a computational experiment with the monthly residential consumption of electricity in Brazil.
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Cyprich, Ondrej, Vladimír Konečný, and Katarína Kiliánová. "Short-Term Passenger Demand Forecasting Using Univariate Time Series Theory." PROMET - Traffic&Transportation 25, no. 6 (2013): 533–41. http://dx.doi.org/10.7307/ptt.v25i6.338.
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The purpose of the paper is to identify and analyse the forecasting performance of the model of passenger demand for suburban bus transport time series, which satisfies the statistical significance of its parameters and randomness of its residuals. Box-Jenkins, exponential smoothing and multiple linear regression models are used in order to design a more accurate and reliable model compared the ones used nowadays. Forecasting accuracy of the models is evaluated by comparative analysis of the calculated mean absolute percent errors of different approaches to forecasting. In accordance with the main goal of the paper was identified the ARIMA model, which fulfils almost all statistical criterions with an exception of the model residuals normality. In spite of the limitation, the best forecasting abilities of identified model have been proven in comparison with other approaches to forecasting in the paper. The published findings of research will have positive influence on increasing the forecasting accuracy in the process of passenger demand forecasting.
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Maulana, Hutomo Atman, Muliah Muliah, Maria Zefaya Sampe, and Farrah Hanifah. "Pemodelan dan Peramalan Deret Waktu Studi Kasus: Suhu Permukaan Laut di Selatan Jawa Timur." Math Educa Journal 1, no. 2 (2017): 187–99. http://dx.doi.org/10.15548/mej.v1i2.26.
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The sea surface temperature is one of the important components that can determine the potential of the sea. This research aims to model and forecast time series data of sea surface temperature by using a Box-Jenkins method. Data in this research are the sea surface temperatures in the South of East Java (January 1983-December 2013) with sample size of 372. 360 data will be used for modeling which is from January 1983 to December 2012, and data in 2013 will be used for forecasting. Based on the results of analysis time series, the appropriate models is SARIMA(1,0,0) (1,0,1)12 where can be written as Yt = 0,010039 + 0,734220Yt−1 + 0,014893Yt−12 − (0,734220)(0,014893)Yt−13 + 0,940726et−12 with MSE of 0.07888096.Keywords: Sea surface temperature, time series, Box-Jenkins method
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Chutiman, Nipaporn, Pannarat Guayjarernpanishk, Monchaya Chiangpradit, Piyapatr Busababodhin, Saowanee Rattanawan, and Butsakorn Kong-Led. "The Forecasting Model with Climate Variables of the Re-emerging Disease Rate in Elderly Patients." WSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT 17 (August 4, 2021): 866–75. http://dx.doi.org/10.37394/232015.2021.17.81.
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This research forecasted the incidence rate per 100,000 elderly population with food poisoning, pneumonia, and fever of unknown origin in Khon Kaen Province and Roi Et Province in the northeastern part of Thailand. In the study, the time series forecasting with Box-Jenkins Method (SARIMA model) and Box-Jenkins Method with climate variables, i.e total monthly rainfall, maximum average monthly temperature, average relative humidity, minimum average monthly temperature and average temperature (SARIMAX model) was performed. The study results revealed that the forecasting accuracy was closely similar to the model without the climate variables in the combined analysis although such climate variables had relationship with the incidence rate per 100,000 elderly population with food poisoning, pneumonia, and fever of unknown origin. Therefore, the appropriate model should be the SARIMA model because it is easier for analysis but with higher forecasting accuracy than the SARIMAX model.
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Shinkarenko, Volodymyr, Alexey Hostryk, Larysa Shynkarenko, and Leonid Dolinskyi. "A forecasting the consumer price index using time series model." SHS Web of Conferences 107 (2021): 10002. http://dx.doi.org/10.1051/shsconf/202110710002.
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This article examines the behavior of the consumer price index in Ukraine for the period from January 2010 to September 2020. The characteristics of the initial time series, the analysis of autocorrelation functions made it possible to reveal the tendency of their development and the presence of annual seasonality. To model the behavior of the consumer price index and forecast for the next months, two types of models were used: the additive ARIMA*ARIMAS model, better known as the model of Box-Jenkins and the exponential smoothing model with the seasonality estimate of Holt-Winters. As a result of using the STATISTICA package, the most adequate models were built, reflecting the monthly dynamics of the consumer price index in Ukraine. The inflation forecast was carried out on the basis of the Holt-Winters model, which has a minimum error.
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Popescu, Th D. "Experiences with a computer aided procedure for time series analysis and forecasting using Box-Jenkins philosophy." Annual Review in Automatic Programming 12 (January 1985): 361–64. http://dx.doi.org/10.1016/0066-4138(85)90062-x.
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Bigović, Miloš. "Demand forecasting within Montenegrin tourism using Box-Jenkins methodology for seasonal ARIMA models." Tourism and hospitality management 18, no. 1 (2012): 1–18. http://dx.doi.org/10.20867/thm.18.1.1.
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The purpose of this paper is to construct adequate seasonal ARIMA models, using Box-Jenkins methodology, and to implement them in order to forecast short run flows of tourist arrivals and tourist overnight stays in Montenegro. Time scope covers ten years, from 2001/01 to 2010/12, while twelve months of 2011 are out-of-sample forecasts. Close inspection of related time series was applied which revealed no extreme and unusual specificities in the data. Therefore, only economic impacts have been affected the time series. This was important because econometric intervention analysis was excluded from models designing and building. As a result, our approach was based on time series modelling without need to take care of any structural breaks. Modified Box-Pierce and Jarque-Bera test statistics confirmed good quality of the models. Further, the results show excellent forecasting performances of specified models. According to forecasting output, Montenegro can expect upgrowth in terms of tourist arrivals as well as in terms of tourist overnight stays. The model has shown around 7,25% rise in arrivals, which is about 91 thousands tourists more in 2011 compared with the previous year. On the other hand, the calculated rise of overnight stays is around 8,42%, or about 670 thousands more than the year before.
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Grigaliūnienė, Žana. "Time-series Models Forecasting Performance in the Baltic Stock Market." Organizations and Markets in Emerging Economies 4, no. 1 (2013): 104–20. http://dx.doi.org/10.15388/omee.2013.4.1.14261.
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Contradicting evidence on time-series and financial analysts’ forecasting performance calls for further research in emerging markets. Motivation to use time-series models rather than analysts’ forecasts stems from recent research that reports time-series predictions to be superior to analysts’ forecasts in predicting earnings for longer periods and for small firms that are hardly followed by financial analysts, especially in emerging markets. The paper aims to explore time-series models performance in forecasting quarterly earnings for Baltic Firms in 2000-2009. The paper uses simple and seasonal random walk models with and without drift, Foster’s, Brown-Rozeff’s and Griffin-Watts’ models to forecast quarterly earnings. It also employs the firm-specific Box-Jenkins methodology to perform time-series analysis for individual firms. Forecasting performance of selected models is compared on the basis of goodness-of-fit statistics. The paper finds that naive time-series models outperform premier ARIMA family models in terms of mean percentage errors and average ranks. The findings suggest that investors use naive models to form their expectations.
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Champion, Robert, Leigh D. Kinsman, Geraldine A. Lee, et al. "Forecasting emergency department presentations." Australian Health Review 31, no. 1 (2007): 83. http://dx.doi.org/10.1071/ah070083.
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Objective: To forecast the number of patients who will present each month at the emergency department of a hospital in regional Victoria. Methods: The data on which the forecasts are based are the number of presentations in the emergency department for each month from 2000 to 2005. The statistical forecasting methods used are exponential smoothing and Box?Jenkins methods as implemented in the software package SPSS version 14.0 (SPSS Inc, Chicago, Ill, USA). Results: For the particular time series, of the available models, a simple seasonal exponential smoothing model provides optimal forecasting performance. Forecasts for the first five months in 2006 compare well with the observed attendance data. Conclusions: Time series analysis is shown to provide a useful, readily available tool for predicting emergency department demand. The approach and lessons from this experience may assist other hospitals and emergency departments to conduct their own analysis to aid planning.
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Kambo, B. S., and Dr Kulwinder Kaur. "Forecasting End of COVID – 19 in India Based on Time Series Analysis." Volume 5 - 2020, Issue 9 - September 5, no. 9 (2020): 763–72. http://dx.doi.org/10.38124/ijisrt20sep543.
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In this paper, the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models for active and exponential smoothing HOLT for removed rates has been estimated using daily time series data from 1st April to 14thSeptember 2020.The active and removed rates are computed from cumulative confirmed, active, recovered and deceased cases. It has been found that ARIMA (0, 1, 1) and Holt exponential smoothing Models are best fit for active and removed rates respectively. Normalized BIC is 0.577and 0.898 for active and removed rates respectively and is minimum among all the six models considered. Lack of fit of models is tested by Ljung-BoX Q statistic. The pvalue is 0.925 and 0.840 for active and removed rates respectively Since for both the rates p-value is greater than 0.05, hence conclude that our model does not show a lack of fit. On the basis of our analysis, active rate will be nullified latest by 5th January 2020, if everything goes best, as P M of India has assured on eve of Independence Day that vaccine for corona will be available very soon. Otherwise by 9 th February 2021 if the past trend continued and in worst situation it will tends to zero on 26th March 2021. We expect the removed rates will reach 100 percent by 20TH October 2020 if everything goes best and by 5th January 2021 if the past trend continued. On the assumptions that Pandemic will come to an end when removed rate in the population tends to 100 percent and active rate to zero percent. Thus on the basis our analysis we expect that COVID – 19 Pandemic may come to end latest either by 9 th February 2021 or 26th March 2021 subject to condition that the social distance and safely measures remains vigilance to stabilize and control the pandemic and in achieving India’s recovery from COVID-19.
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Xie, Liming. "An Experimental Data of Lithium-Ion Battery Time Series Analysis." International Journal of Data Analytics 2, no. 2 (2021): 1–26. http://dx.doi.org/10.4018/ijda.2021070101.
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The experimental data of Lithium-ion battery has its specific sense. This paper is proposed to analyze and forecast it by using autoregressive integrated moving average (ARIMA) and spectral analysis, which has effective and statistical results. The method includes the identification of the data, estimation and diagnostic checking, and forecasting the future values by Box and Jenkins. The analysis shows that the time series models are related with the present value of a series to past values and past prediction errors. After transferring the data by different function, improving autocorrelations are significant. Forecasting the future values of the possible observations show significantly fluctuated such as increasing or decreasing in specific ranges accordingly. In spectral analysis, the parameters of the model were determined by performing spectral analysis of the experimental data to look periodicities or cyclical patterns, and to check the existence of white noise in the data. The Bartlett's Kolmogorov-Smirnov statistic suggests the white noise of the data. The spectral analysis for the series reveals non-11-second cycle of activity for dynamic stress test current, but strong 45-second that highlights the position of the main peak in the spectral density; strong 21-second and 45-second for the urbane dynamometer driver schedule current and voltage, respectively; but no significance for dynamic stress test current.
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Iqbal, Muhammad, and Amjad Naveed. "Forecasting Inflation: Autoregressive Integrated Moving Average Model." European Scientific Journal, ESJ 12, no. 1 (2016): 83. http://dx.doi.org/10.19044/esj.2016.v12n1p83.
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This study compares the forecasting performance of various Autoregressive integrated moving average (ARIMA) models by using time series data. Primarily, The Box-Jenkins approach is considered here for forecasting. For empirical analysis, we used CPI as a proxy for inflation and employed quarterly data from 1970 to 2006 for Pakistan. The study classified two important models for forecasting out of many existing by taking into account various initial steps such as identification, the order of integration and test for comparison. However, later model 2 turn out to be a better model than model 1 after considering forecasted errors and the number of comparative statistics.
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Pamungkas, Muhammad Bintang. "APLIKASI METODE ARIMA BOX-JENKINS UNTUK MERAMALKAN KASUS DBD DI PROVINSI JAWA TIMUR." Indonesian Journal of Public Health 13, no. 2 (2019): 183. http://dx.doi.org/10.20473/ijph.v13i2.2018.183-196.
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The Box-Jenkins forecasting method is one of the time series forecasting methods. This method uses past values as dependent variables and independent variables are ignored. Box-Jenkins (ARIMA) method has advantages that can be used on non-stationary data, can be used on all data patterns including seasonal data patterns so this method can be used to predict cases of DHF in East Java Province. This research was conducted to determine the best model with seasonal ARIMA forecasting model and also to analyze the result of DHF case forecasting in East Java Province. The analysis result shows that the best model for DHF case in East Java Province is ARIMA (1,1,2)(2,1,1)12. The best model has fulfilled the test requirement that is parameter significance test and diagnostics check. Forecasting results show the number of DHF cases in 2017-2018 will experience an upward trend. The total number of DHF cases in 2017 was 14,277 cases and increased to 22,284.54 DHF cases in 2018. The forecasting results showed that the highest peak of DHF cases occurred in January 2017 with 1,914.22 cases and then decrease in the next month until the lowest case occurred in October with 768.46. The forecast for 2018 also shows that the highest DHF cases occurred in January with 3455.55 and declined to the lowest in October with 1126.49 cases. MAPE value in the forecast is 43.51%. The MAPE value indicates that the forecasting is good enough, adequate and feasible to use.
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Sebri, Maamar. "ANN versus SARIMA models in forecasting residential water consumption in Tunisia." Journal of Water, Sanitation and Hygiene for Development 3, no. 3 (2013): 330–40. http://dx.doi.org/10.2166/washdev.2013.031.
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Water scarcity and increasing water demand, especially for residential end-use, are major challenges facing Tunisia. The need to accurately forecast water consumption is useful for the planning and management of this natural resource. In the current study, quarterly time series of household water consumption in Tunisia was forecast using a comparative analysis between the traditional Box–Jenkins method and an artificial neural networks approach. In particular, an attempt was made to test the effectiveness of data preprocessing, such as detrending and deseasonalization, on the accuracy of neural networks forecasting. Results indicate that the traditional Box–Jenkins method outperforms neural networks estimated on raw, detrended, or deseasonalized data in terms of forecasting accuracy. However, forecasts provided by the neural network model estimated on combined detrended and deseasonalized data are significantly more accurate and much closer to the actual data. This model is therefore selected to forecast future household water consumption in Tunisia. Projection results suggest that by 2025, water demand for residential end-use will represent around 18% of the total water demand of the country.
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Zheng, Yanling, Xueliang Zhang, Xijiang Wang, Kai Wang, and Yan Cui. "Predictive study of tuberculosis incidence by time series method and Elman neural network in Kashgar, China." BMJ Open 11, no. 1 (2021): e041040. http://dx.doi.org/10.1136/bmjopen-2020-041040.
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ObjectivesKashgar, located in Xinjiang, China has a high incidence of tuberculosis (TB) making prevention and control extremely difficult. In addition, there have been very few prediction studies on TB incidence here. We; therefore, considered it a high priority to do prediction analysis of TB incidence in Kashgar, and so provide a scientific reference for eventual prevention and control.DesignTime series study.Setting Kashgar, ChinaKashgar, China.MethodsWe used a single Box-Jenkins method and a Box-Jenkins and Elman neural network (ElmanNN) hybrid method to do prediction analysis of TB incidence in Kashgar. Root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) were used to measure the prediction accuracy.ResultsAfter careful analysis, the single autoregression (AR) (1, 2, 8) model and the AR (1, 2, 8)-ElmanNN (AR-Elman) hybrid model were established, and the optimal neurons value of the AR-Elman hybrid model is 6. In the fitting dataset, the RMSE, MAE and MAPE were 6.15, 4.33 and 0.2858, respectively, for the AR (1, 2, 8) model, and 3.78, 3.38 and 0.1837, respectively, for the AR-Elman hybrid model. In the forecasting dataset, the RMSE, MAE and MAPE were 10.88, 8.75 and 0.2029, respectively, for the AR (1, 2, 8) model, and 8.86, 7.29 and 0.2006, respectively, for the AR-Elman hybrid model.ConclusionsBoth the single AR (1, 2, 8) model and the AR-Elman model could be used to predict the TB incidence in Kashgar, but the modelling and validation scale-dependent measures (RMSE, MAE and MAPE) in the AR (1, 2, 8) model were inferior to those in the AR-Elman hybrid model, which indicated that the AR-Elman hybrid model was better than the AR (1, 2, 8) model. The Box-Jenkins and ElmanNN hybrid method therefore can be highlighted in predicting the temporal trends of TB incidence in Kashgar, which may act as the potential for far-reaching implications for prevention and control of TB.
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Ionela, Costică, and Boitan Iustina Alina. "EUR/RON EXCHANGE RATE PREDICTION BASED ON BOX-JENKINS TECHNIQUE." SWS Journal of SOCIAL SCIENCES AND ART 1, no. 2 (2019): 31–41. http://dx.doi.org/10.35603/ssa2019/issue2.03.
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The aim of this study consists in analyzing the importance of the exchange rate forecast using the Box-Jenkins models, also known as Auto Regressive Integrated Moving Average (ARIMA) models. The first part of the paper presents the main research in this field, which can be classified in two categories (studies applying classical methods, such as Box-Jenkins models and studies which rely on sophisticated prediction tools), and summarizes the main findings of some of the studies applying Box-Jenkins models. In the second part of the paper we performed a EUR/RON exchange rate analysis and forecasting, based on testing several AR, MA and ARMA candidate processes, in order to find out the best fitting model specification. We adopted the following strategy: i) an initial time series had been used for testing various model specifications, identify the best performing one and making a forecast of the EUR/RON exchange rate; ii) after comparing the accuracy of this forecast with the real level recorded by the exchange rate at end of May 2018, we conducted a second forecast, for the period May 2019 – November 2019. The initial time series employed has daily frequency and covers the timeframe July 4, 2005 – December 5, 2017, while the second time series used covers the period July 4, 2005 – May 6, 2019. The empirical findings have passed the goodness-of-fit tests and show a good predictive power. The first forecast performed for a six month period (December 2017 – May 2018) has indicated a slow pace, persistent increase of the EUR/RON exchange rate, which was confirmed by the expectations of market participants (financial analysts, banks’ research departments). The second forecast, which covers the period May 2019 – November 2019, indicates a similar rising trend and the ongoing depreciation of the national currency.
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Ohakwe, J., I. V. Odo, and C. Nwosu. "A Statistical Analysis of the Nigerian External Reserveand the Impact of Military and Civilian Rule." Bulletin of Mathematical Sciences and Applications 3 (February 2013): 49–62. http://dx.doi.org/10.18052/www.scipress.com/bmsa.3.49.
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In this paper, the Nigerian External Reserve (ER) for the period 1960 – 2010 was modeled using descriptive time series technique and Box-Jenkins (ARIMA) model. Prior to the analysis the logarithm transformation was found to be the most appropriate to stabilize the variance of the data after the Bartlett's test of homogeneity of variance suggested non-constant variance. Applying the descriptive time series technique on the transformed data, a linear trend was found adequate which suggest an exponential trend for the untransformed data. However, the seasonal indexes were found to be insignificant which implies that the data is completely dominated by the trend. Furthermore, considering that the model obtained using the descriptive time series technique was found inadequate as suggested by the autocorrelation function (ACF) of the irregular component and therefore cannot be used for forecasting, a Box-Jenkins model was then fitted and was found adequate as suggested by the p-value = 0.00 for the modelsignificance. Furthermore using the Relative Percentage Change (RPC) to assess the impact ofthe various regimes on the ER data, it was found that the regimes of General Yakubu Gowon (Rtd) and Alhaji Shehu Shagari respectively had the most positive and negative impact on the ER data. Finally using the cumulative RPC in assessing the impact of civilian and military regimes on the ER data, it was discovered that the military had a higher positive impact than the civilian regimes.
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Wu, Chien Ho. "ARIMA Models are Clicks Away." Applied Mechanics and Materials 411-414 (September 2013): 1129–33. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1129.
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It is often the case that managers and social scientists are called to deal with time series. Time series analysis usually involves a study of the components of the time series and finding models that permit statistical inferences and predictions. ARIMA models are, in theory, the most general class of models for forecasting a time series. The commonly known Box-Jenkins approach to ARIMA model building is an iterative process. To facilitate the iterative process and to relieve the boredom of computational errands, we have developed an assistor for building ARIMA models. The assistor is implemented in Java with embedded R for statistical functions. With the help of the assistor ARIMA models for time series are few clicks away, thus enabling users to focus their efforts on the decision problems at hand.
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Pardede, Paiaman, Maurits Sipahutar, and Parulian Naibaho. "Forecasting Stock Prices of PT. Bank Negara Indonesia (Persero) Tbk., by Method (BOX-JENKINS)." Primanomics : Jurnal Ekonomi & Bisnis 19, no. 1 (2021): 191. http://dx.doi.org/10.31253/pe.v19i1.520.
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The purpose of this study is to find the most appropriate model for predicting future stock prices, and the analytical tool used is ARIMA. In this study, the authors used the time series data of the share price of PT BNI (Persero) Tbk. from January 3, 2017, to June 28, 2019, consisting of 594 working days from the Investing.com database. The research found that the ARIMA model analysis (3,1,3) is the most appropriate model for predicting the share price of PT. Bank Negara Indonesia (Persero) Tbk, with the equation model: Yt = - 6.331988 + 1.714721Yt-1 - 0.149406 Yt-2 - 1.72221 Y t-3 + 0.858083 Yt-4 + 0.729283 t-1 - 0.845787 t-2 - 0.898101 t-3.
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Carrasco Choque, Freddy, Mario Villegas Yarleque, and Janet Del Rocio Sanchez Castro. "Análisis univariante para describir y pronosticar la producción de plátano en la región de piura." Universidad Ciencia y Tecnología 25, no. 109 (2021): 71–79. http://dx.doi.org/10.47460/uct.v25i109.450.
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La actividad agrícola en la región de Piura, es una actividad fundamental para su desarrollo, la implementación de pronósticos es una herramienta útil para los agentes económicos para una planificación y toma de decisiones acertadas. En el estudio interesan dos resultados, el primero identificar, estimar y validar un modelo ajustado para pronosticar la producción de plátano y el segundo realizar el pronóstico de la producción de plátano para el periodo de octubre de 2020 hasta octubre de 2022. Para concretizar los objetivos se realizó el análisis univariante con la metodología de Box y Jenkins. Los datos provienen del Banco Central de Reserva del Perú, se consideraron datos mensuales desde julio de 2000 hasta septiembre de 2020. Luego del cumplimiento de los supuestos, el mejor modelo ajustado para representar la producción del plátano y realizar pronósticos es un modelo autorregresivo integrado de promedio móvil o ARIMA. El pronóstico de la producción del plátano tiene una tendencia decreciente para los próximos años.
 Palabras Clave: Pronostico, Series de tiempo, Modelos ARIMA, Producción agrícola.
 Referencias
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 [17]L. Laurente, “Proyección de la producción de papa en puno. una aplicación de la metodología de Box-Jenkins,” Semest. Econ. - FIE - UNA Puno, 2018.[18]Banco Central de Reserva del Perú, “Gerencia Central de Estudios Económicos,” 2019. [Online]. Available: https://estadisticas.bcrp.gob.pe/estadisticas/series/mensuales/resultados/PN01784AM/html.
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Belotti, Jonatas, José Jair Mendes, Murilo Leme, Flavio Trojan, Sergio L. Stevan, and Hugo Siqueira. "Comparative study of forecasting approaches in monthly streamflow series from Brazilian hydroelectric plants using Extreme Learning Machines and Box & Jenkins models." Journal of Hydrology and Hydromechanics 69, no. 2 (2021): 180–95. http://dx.doi.org/10.2478/johh-2021-0001.
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Abstract Several activities regarding water resources management are dependent on accurate monthly streamflow forecasting, such as flood control, reservoir operation, water supply planning, hydropower generation, energy matrix planning, among others. Most of the literature is focused on propose, compare, and evaluate the forecasting models. However, the decision on forecasting approaches plays a significant role in such models’ performance. In this paper, we are focused on investigating and confront the following forecasting approaches: i) use of a single model for the whole series (annual approach) versus using 12 models, each one responsible for predicting each month (monthly approach); ii) for multistep forecasting, the use of direct and recursive methods. The forecasting models addressed are the linear Autoregressive (AR) and Periodic Autoregressive (PAR) models, from the Box & Jenkins family, and the Extreme Learning Machines (ELM), an artificial neural network architecture. The computational analysis involves 20 time series associated with hydroelectric plants indicated that the monthly approach with the direct multistep method achieved the best overall performances, except for the cases in which the coefficient of variation is higher than two. In this case, the recursive approach stood out. Also, the ELM overcame the linear models in most cases.
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P. NYONI, Smartson, and Thabani NYONI. "Adults newly infected with hiv in burundi: a box-jenkins arima approach." Middle European Scientific Bulletin 4 (September 30, 2020): 48–56. http://dx.doi.org/10.47494/mesb.2020.4.40.
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Using annual time series data on the number of adults (ages 15 and above) newly infected with HIV in Burundi from 1990 – 2018, the study predicts the annual number of adults who will be newly infected with HIV over the period 2019 – 2025. The study applied the Box-Jenkins ARIMA methodology. The diagnostic ADF tests as well as correlogram analysis show that the G series under consideration is an I (2) variable. Based on the AIC, the study presents the ARIMA (0, 2, 1) model as the optimal model. The residual correlogram and the inverse roots of the applied model further reveal that the presented model is stable and suitable for forecasting new HIV infections in adults in Burundi. The results of the study indicate that the number of new HIV infections in adults in Burundi will most likely decline, over the period 2019 – 2023, from approximately 698 to almost 90 new HIV infections. By 2025, Burundi could experience her first zero new HIV infections in adults! This implies that, despite the fact that Vision Burundi 2025 is a highly ambitious blue-print; Vision Burundi 2025 will largely be achieved as far as HIV/AIDS prevention and control is concerned.
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Abd Al-zahra, Khadeega, Khulood Moosa, and Basil Jasim. "A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques." Iraqi Journal for Electrical and Electronic Engineering 11, no. 1 (2015): 110–23. http://dx.doi.org/10.37917/ijeee.11.1.12.
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The electrical consumption in Basra is extremely nonlinear; so forecasting the monthly required of electrical consumption in this city is very useful and critical issue. In this Article an intelligent techniques have been proposed to predict the demand of electrical consumption of Basra city. Intelligent techniques including ANN and Neuro-fuzzy structured trained. The result obtained had been compared with conventional Box-Jenkins models (ARIMA models) as a statistical method used in time series analysis. ARIMA (Autoregressive integrated moving average) is one of the statistical models that utilized in time series prediction during the last several decades. Neuro-Fuzzy Modeling was used to build the prediction system, which give effective in improving the predict operation efficiency. To train the prediction system, a historical data were used. The data representing the monthly electric consumption in Basra city during the period from (Jan 2005 to Dec 2011). The data utilized to compare the proposed model and the forecasting of demand for the subsequent two years (Jan 2012-Dec 2013). The results give the efficiency of proposed methodology and show the good performance of the proposed Neuro-fuzzy method compared with the traditional ARIMA method.
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Pandey, Kamal, and Bhaskar Basu. "Mathematical modeling for short term indoor room temperature forecasting using Box-Jenkins models." Journal of Modelling in Management 15, no. 3 (2020): 1105–36. http://dx.doi.org/10.1108/jm2-08-2019-0182.
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Purpose The rapid urbanization of Indian cities and the population surge in cities has steered a massive demand for energy, thereby increasing the carbon emissions in the environment. Information and technology advancements, aided by predictive tools, can optimize this energy demand and help reduce harmful carbon emissions. Out of the multiple factors governing the energy consumption and comfort of buildings, indoor room temperature is a critical one, as it envisages the need for regulating the temperature. This paper aims to propose a mathematical model for short-term forecasting of indoor room temperature in the Indian context to optimize energy consumption and reduce carbon emissions in the environment. Design/methodology/approach A study is conducted to forecast the indoor room temperature of an Indian corporate building structure, based upon various external environmental factors: temperature and rainfall and internal factors like cooling control, occupancy behavior and building characteristics. Expert insight and principal component analysis are applied for appropriate variables selection. The machine learning approach using Box–Jenkins time series models is used for the forecasting of indoor room temperature. Findings ARIMAX model, with lagged forecasted and explanatory variables, is found to be the best-fit model. A predictive short-term hourly temperature forecasting model is developed based upon ARIMAX model, which yields fairly accurate results for data set pertaining to the building conditions and climatic parameters in the Indian context. Results also investigate the relationships between the forecasted and individual explanatory variables, which are validated using theoretical proofs. Research limitations/implications The models considered in this research are Box–Jenkins models, which are linear time series models. There are non-linear models, such as artificial neural network models and deep learning models, which can be a part of this study. The study of hybrid models including combined forecasting techniques comprising linear and non-linear methods is another important area for future scope of study. As this study is based on a single corporate entity, the models developed need to be tested further for robustness and reliability. Practical implications Forecasting of indoor room temperature provides essential practical information about meeting the in-future energy demand, that is, how much energy resources would be needed to maintain the equilibrium between energy consumption and building comfort. In addition, this forecast provides information about the prospective peak usage of air-conditioning controls within the building indoor control management system through a feedback control loop. The resultant model developed can be adopted for smart buildings within Indian context. Social implications This study has been conducted in India, which has seen a rapid surge in population growth and urbanization. Being a developing country, India needs to channelize its energy needs judiciously by minimizing the energy wastage and reducing carbon emissions. This study proposes certain pre-emptive measures that help in minimizing the consumption of available energy resources as well as reducing carbon emissions that have significant impact on the society and environment at large. Originality/value A large number of factors affecting the indoor room temperature present a research challenge for model building. The paper statistically identifies the parameters influencing the indoor room temperature forecasting and their relationship with the forecasted model. Considering Indian climatic, geographical and building structure conditions, the paper presents a systematic mathematical model to forecast hourly indoor room temperature for next 120 h with fair degree of accuracy.
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Suman, Suman, and Urmil Verma. "State space modelling and forecasting of sugarcane yield in Haryana, India." Journal of Applied and Natural Science 9, no. 4 (2017): 2036–42. http://dx.doi.org/10.31018/jans.v9i4.1485.
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Box and Jenkins’ Autoregressive Integrated Moving Average (ARIMA) models are widely used for analyzing and forecasting the time-series data. In this approach, the underlying parameters are assumed to be constant however the data in agriculture are generally collected over time and thus have the time-dependency in parameters. Such data can be analyzed using state space (SS) procedures by the application of Kalman filtering technique. The purpose of this article is to illustrate the usefulness of state space models in sugarcane yield forecasting and to pro-vide some empirical evidence for its superiority over the classical time-series analysis. ARIMA and state space models individually could provide the suitable relationship(s) to reliably forecast the sugarcane yield in Karnal, Ambala, Kurukshetra, Yamunanagar and Panipat districts of Haryana (India). However, the state space models with lower error metrics showed the superiority over ARIMA models for this empirical study. The sugarcane yield forecasts based on SS models in the districts under consideration showed good agreement with State Department of Agriculture (DOA) yields by showing 3-6 percent average absolute deviations.
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Jelena, Mladenovic, Ilic Ivana, and Kostic Zorana. "Modeling The Unemployment Rate At The Eu Level By Using Box-Jenkins Methodology." KnE Social Sciences 1, no. 2 (2017): 1. http://dx.doi.org/10.18502/kss.v1i2.643.
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<p>Unemployment, as a measure of market conditions, appears as a crucial economic problem and a phenomenon with considerable negative social consequences, and, as such, requires attention and adequate approach to finding solutions. Enormous unemployment rates are a reality not only in developing and transition countries, but also in some developed countries. Inadequately conducted privatization, unsuccessful transfer of workers from the public to the private sector, inefficiency in attracting foreign direct investment, and the world economic crisis of 2008 have made unemployment a universal disease of modern society. The paper presents economic models in which the unemployment rate is the central analyzed phenomenon. In this context, an important task of European economic policy-makers is to project future unemployment rates. <em>Box-Jenkins</em> methodology, i.e. the seasonal ARIMA model, is one approach to the modeling of time series, or, more specifically, for forecasting future values. The subject of this paper is the analysis of the evolution of the unemployment problem on the basis of the values in the period from 2000 to 2015, based on the case of 28 countries of the European Union. Building on the research subject, the purpose of the paper is to create the statistical model for forecasting the values of the monthly unemployment rates in the European Union for the future and establishing its trend.</p>
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Matskul, Valerii, Diana Okara, and Nataliia Podvalna. "The Ukraine and EU trade balance: prediction via various models of time series." SHS Web of Conferences 73 (2020): 01020. http://dx.doi.org/10.1051/shsconf/20207301020.
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This article is the first to study, simulate and forecast the monthly dynamics of the trade balance between Ukraine and the European Union for the period from 2005 to 2019. In the presented work, three types of models were used for modeling and forecasting: Automated Neural Networks, additive models ARIMA *ARIMAS (Autoregressive integrated moving average with season component) and Holts model with a damped trend. When modeling using the Automated Neural Networks module, various ensembles of networks and neuron activation functions in hidden layers were used. It turned out that Automated Neural Networks have poor prognostic ability (as in the case considered by us, when modeling insufficiently long series of dynamics). Therefore, when modeling and forecasting the dynamics of the Ukraine-EU trade balance, classical (so-called Box-Jenkins) time series models were used. In this case, the time series is divided into several components (in our case, terms): the main trend is the trend, the seasonal component and the random component (the so-called white noise). By smoothing the initial series, a trend was found, and an analysis of the autocorrelation functions revealed a one-year seasonality. Eliminating the trend and the seasonal component, we obtained a random component, which has a Gaussian distribution. This made it possible to apply first the ARIMA* ARIMAS additive model, and then the Holt model of exponential smoothing with a damped trend. Adequate models of Ukraine-EU trade balance dynamics have been obtained, according to which the forecast has been made. A comparative analysis of the models used. The best model was chosen for forecasting, which allowed to get a good forecast (in comparison with actual data).
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Moro, Matheus Fernando, Andreas Dittmar Weise, and Antonio Cezar Bornia. "Model Hybrid for Sales Forecast for the Housing Market of São Paulo." Real Estate Management and Valuation 28, no. 3 (2020): 45–64. http://dx.doi.org/10.1515/remav-2020-0023.
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AbstractThis research proposes a combined model of time series for forecasting housing sales in the city of São Paulo. We used data referring to the time series of sales of residential units provided by SECOVI-SP. The Exponential Softening, Box-Jenkins and Artificial Neural Networks models are individually modelled, later these are combined through five forecast combination techniques.The techniques used are Arithmetic Mean, Geometric Mean, Harmonic Mean, Linear Regression and Principal Component Analysis. The measures of accuracy to measure the results obtained and to select the best model are the RMSE, MAPE and UTheil of forecast. The results showed that Linear Regression with an independent variable, being a combination of the SARIMA model (2,0,0)(2,0,0)12 and MLP/RNA (12,10,1), provided a satisfactory performance, with an RMSE of 368.74, MAPE of 19.2% and UTheil of 0.315.The combination of time series models allowed a significant increase in forecast performance. Finally, the model was validated, using it to predict housing sales. The results show that the model has a good fit, thus demonstrating that using a housing sales forecasting model helps industry professionals minimize error and make sales and launch decisions.
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Maxwell, Obubu, Ikediuwa Udoka Chinedu, Anabike Charles Ifeanyi, and Nwokike Chukwudike C. "On Modeling Murder Crimes in Nigeria." Scientific Review, no. 58 (August 1, 2018): 157–62. http://dx.doi.org/10.32861/sr.58.157.162.
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This paper examines the modelling and forecasting Murder crimes using Auto-Regressive Integrated Moving Average models (ARIMA). Twenty-nine years data obtained from Nigeria Information Resource Center were used to make predictions. Among the most effective approaches for analyzing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). The augmented Dickey-Fuller test for unit root was applied to the data set to investigate for Stationarity, the data set was found to be non-stationary hence transformed using first-order differencing to make them Stationary. The Stationarities were confirmed with time series plots. Statistical analysis was performed using GRETL software package from which, ARIMA (0, 1, 0) was found to be the best and adequate model for Murder crimes. Forecasted values suggest that Murder would slightly be on the increase.
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Musial, Nayane Thais Krespi, and Anselmo Chaves Neto. "Metodologia box and jenkins e análise de dados em painel na previsão de séries financeiras / Box and jenkins methodology and panel data analysis in financial series forecasting." Brazilian Journal of Business 3, no. 1 (2021): 78–93. http://dx.doi.org/10.34140/bjbv3n1-005.
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Gregório, Vera, Dinilson Pedroza, Celivane Barbosa, et al. "Predicting the detection of leprosy in a hyperendemic area of Brazil: Using time series analysis." Indian Journal of Dermatology, Venereology and Leprology 87 (February 1, 2021): 651–59. http://dx.doi.org/10.25259/ijdvl_1082_19.
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Background: Brazil has the second highest prevalence of leprosy worldwide. Autoregressive integrated moving average models are useful tools in surveillance systems because they provide reliable forecasts from epidemiological time series. Aim: To evaluate the temporal patterns of leprosy detection from 2001 to 2015 and forecast for 2020 in a hyperendemic area in northeastern Brazil. Methods: A cross-sectional study was conducted using monthly leprosy detection from the Brazil information system for notifiable diseases. The Box–Jenkins method was applied to fit a seasonal autoregressive integrated moving average model. Forecasting models (95% prediction interval) were developed to predict leprosy detection for 2020. Results: A total of 44,578 cases were registered with a mean of 247.7 cases per month. The best-fitted model to make forecasts was the seasonal autoregressive integrated moving average ((1,1,1); (1,1,1)). It was predicted 0.32 cases/100,000 inhabitants to January of 2016 and 0.38 cases/100,000 inhabitants to December of 2020. Limitations: This study used secondary data from Brazil information system for notifiable diseases; hence, leprosy data may be underreported. Conclusion: The forecast for leprosy detection rate for December 2020 was < 1 case/100,000 inhabitants. Seasonal autoregressive integrated moving average model has been shown to be appropriate and could be used to forecast leprosy detection rates. Thus, this strategy can be used to facilitate prevention and elimination programmes.
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Choudhury, Avishek, and Estefania Urena. "Forecasting hourly emergency department arrival using time series analysis." British Journal of Healthcare Management 26, no. 1 (2020): 34–43. http://dx.doi.org/10.12968/bjhc.2019.0067.
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Background/aims The stochastic arrival of patients at hospital emergency departments complicates their management. More than 50% of a hospital's emergency department tends to operate beyond its normal capacity and eventually fails to deliver high-quality care. To address this concern, much research has been carried out using yearly, monthly and weekly time-series forecasting. This article discusses the use of hourly time-series forecasting to help improve emergency department management by predicting the arrival of future patients. Methods Emergency department admission data from January 2014 to August 2017 was retrieved from a hospital in Iowa. The auto-regressive integrated moving average (ARIMA), Holt–Winters, TBATS, and neural network methods were implemented and compared as forecasters of hourly patient arrivals. Results The auto-regressive integrated moving average (3,0,0) (2,1,0) was selected as the best fit model, with minimum Akaike information criterion and Schwartz Bayesian criterion. The model was stationary and qualified under the Box–Ljung correlation test and the Jarque–Bera test for normality. The mean error and root mean square error were selected as performance measures. A mean error of 1.001 and a root mean square error of 1.55 were obtained. Conclusions The auto-regressive integrated moving average can be used to provide hourly forecasts for emergency department arrivals and can be implemented as a decision support system to aid staff when scheduling and adjusting emergency department arrivals.
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Butler, M. B., H. Gu, T. Kenney, and S. G. Campbell. "P017: Does a busy day predict another busy day? A time-series analysis of multi-centre emergency department volumes." CJEM 18, S1 (2016): S83—S84. http://dx.doi.org/10.1017/cem.2016.193.
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Introduction: Variations of patient volumes in the ED according to days of the week and month of the year are well-established. Anecdotally, ED volumes follow ‘waves’ that correlate with previous days. Time-series models have traditionally been used in econometrics to develop financial models, but have been adapted in other fields, such as health informatics. This study uses a time-series approach to assess whether these impressions are valid. Methods: The daily volume of patients presenting to four emergency departments (ED) at the Nova Scotia Health Authority from Jan 2010 to May 2015 were analyzed to assess for the effect of previous volumes on future volumes. Parameters were selected using the auto-correlation (ACF) and partial auto-correlation functions (PACF) for a Seasonal Auto-regressive Integrated Moving Average (SARIMA) model. The Box-Jenkins statistic was assessed for model suitability. To assess for accuracy, a forecast of the model was evaluated with a year of volumes set aside for testing. Results: The EDs saw an average of 365.1 patients per day, with a minimum of 188 patients and a maximum of 479. The increasing trend in volumes consistent with the increasing number of ED presentations nation-wide was detrended using linear regression. There was a significant correlation in ACF with the previous day (ρ1 = 0.297). A seasonal, periodic trend was seen weekly. Significant correlations occurred annually (ρ365 = 0.279) and at 29 days (ρ29 = 0.339), consistent with the lunar cycle. A seasonal model was postulated incorporating an auto-regressive (AR) coefficient, and a moving average (MA) coefficient for the previous day’s volume. An AR and MA seasonal coefficient were each incorporated using the weekly period. When using the model on the test data, the model predicted 4 more patient presentations on average than the true value, with 90% of the values within 37 presentations of the true volume. The Box-Jenkins statistic was non-significant, indicating no problems with model specification. Conclusion: The volume of patients presenting to an ED system is correlated with that of the previous day. A weekly seasonal variation was confirmed. Auto-correlations also occur annually and possibly associated with the lunar cycle. Previous ED volumes may be useful in forecasting patient volumes. The time-series approach may discover further ways to predict ED volumes.
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Kurilin, B. L., V. Y. Kisselevskaya-Babinina, N. A. Karasyov, I. V. Kisselevskaya-Babinina, E. V. Kislukhkina, and V. A. Vasilyev. "Selection of Prediction Method of Basic Statistical Work Parameters of N.V. Sklifosovsky Research Institute for Emergency Medicine of the Moscow Healthcare Department." Russian Sklifosovsky Journal "Emergency Medical Care" 8, no. 3 (2019): 246–56. http://dx.doi.org/10.23934/2223-9022-2019-8-3-246-256.
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Background The most important part of the state social and economic policy is optimization of the healthcare system, where the loss of public health leads to economic damage. Against this background, forecasting the work of medical institutions is the basis for the successful development of healthcare, despite the fact that the healthcare system, indicators and standards of medical and social welfare are still not stable, and a clear development strategy for the shortand long-term period has not been worked out. Aim of study Determining the most optimal method for predicting the work of a medical institution, based on identification of the main trends in the time series when constructing a model of the dependence of parameters or determining the behavior of data as a stochastic series (i.e. modeling random processes and random events with some random error).Material and methods To predict the main statistical indicators of N.V. Sklifosovsky Research Institute for Emergency Medicine based on a retrospective analysis, data were used that were submitted to the City Bureau of Medical Statistics and entered into official reporting forms (form № 30, approved by Goskomstat of the Russian Federation dated September 10, 2002, № 175): the number of hospitalized patients and mortality rates in inpatient and intensive care units. To select the optimal methodology for the experimental forecast model, data were used for the period from 1991 to 2016. Indicators for 2017 were taken as control values.Results As a result of the comparison of several methods (moving averages, least squares approach, Brown model, Holt–Winters method, autocorrelation model, Box–Jenkins method) as applied to the work of N.V. Sklifosovsky Research Institute for Emergency Medicine, the Holt–Winters model was chosen as the most appropriate one for the data characteristics.Findings 1. When using methods of moving averages, least squares, Box-Jenkins, as well as Brown model and autocorrelation, the forecast result is not always influenced by strictly straight-line indicators of the time series, due to the heterogeneity of the time series and the presence of outliers (often found in a medical institution providing emergency care), which lead to a significant decrease in the reliability of forecasting. 2. The application of the Holt–Winters model, which takes into account the exponential trend (the trend of time series indicators) and additive season (periodic fluctuations observed in the time series), is most suitable for processing statistical data and forecasting for long-term, medium-term and short-term periods taking the specifics of a hospital providing emergency care into account. 3. The choice of the optimal method for predicting the work of a medical institution, based on the identification of the main trends in the time series, taking most of the features in the modeling of random processes and events into account, allowed to reduce the relative forecast error.
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Ferrão, João L., Dominique Earland, Anísio Novela, Roberto Mendes, Alberto Tungadza, and Kelly M. Searle. "Malaria Temporal Variation and Modelling Using Time-Series in Sussundenga District, Mozambique." International Journal of Environmental Research and Public Health 18, no. 11 (2021): 5692. http://dx.doi.org/10.3390/ijerph18115692.
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Malaria is one of the leading causes of morbidity and mortality in Mozambique, which has the fifth highest prevalence in the world. Sussundenga District in Manica Province has documented high P. falciparum incidence at the local rural health center (RHC). This study’s objective was to analyze the P. falciparum temporal variation and model its pattern in Sussundenga District, Mozambique. Data from weekly epidemiological bulletins (BES) was collected from 2015 to 2019 and a time-series analysis was applied. For temporal modeling, a Box-Jenkins method was used with an autoregressive integrated moving average (ARIMA). Over the study period, 372,498 cases of P. falciparum were recorded in Sussundenga. There were weekly and yearly variations in incidence overall (p < 0.001). Children under five years had decreased malaria tendency, while patients over five years had an increased tendency. The ARIMA (2,2,1) (1,1,1) 52 model presented the least Root Mean Square being the most appropriate for forecasting. The goodness of fit was 68.15% for malaria patients less than five years old and 73.2% for malaria patients over five years old. The findings indicate that cases are decreasing among individuals less than five years and are increasing slightly in those older than five years. The P. falciparum case occurrence has a weekly temporal pattern peaking during the wet season. Based on the spatial and temporal distribution using ARIMA modelling, more efficient strategies that target this seasonality can be implemented to reduce the overall malaria burden in both Sussundenga District and regionally.
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Sen, Aritra, and Shalmoli Dutta. "An Analysis of Time-Series Models for Age-Specific Mortality Rates in India." Volume 5 - 2020, Issue 8 - August 5, no. 8 (2020): 1133–40. http://dx.doi.org/10.38124/ijisrt20aug755.
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Mortality is a continuous force of attrition, tending to reduce the population, a prime negative force in the balance of vital processes (Bhasin and Nag, 2004). Sample Registration System (SRS) serves as the only source of annual data on vital events on a full scale from 1969-70 in India. Few studies have examined the trends and patterns of mortality across time and regions in India (Preston and Bhat, 1984). The Under 5 Mortality Rates (U5MR) can be seen to decrease by more than half from 1970 to 2017 but in contrast little is known about the mortality patterns of the older children (5-9) and young adolescents (10-14), and not many studies have been done on their changing trends (Masquelier et al., 2018). Using the annual data for the 5-14 age, the trend of decline in the mortality patterns is studied from 1970 to 2013. The linear trend in the time series plot suggests analysis using time series models AR(p), MA(q), ARMA(p,q), Box- Jenkins ARIMA(p,d,q) and Random Walk with drift models to get the best fit to the trend of the data. The order of the time series models have been calculated by studying the ACF, PACF plots and the coefficients have been derived using the Yule-Walker equation matrix. An in-sample forecast of the years 2014-17 are taken. The Mean Squared Error (MSE) and the Mean Absolute Percentage Error (MAPE) as a measure of accuracy is used to determine the best fit model. ARIMA(3,1,1) produced lower values making it the best-fit model. Out-of-sample forecasting was done for 2018-2025. The forecast value shows that at the current trend, India would have 0.03 deaths per 1000 population in the 5-14 age group in 2025 showing that the government’s policies and health care interventions towards realization of the MDG4 goal is working positively.
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Boitan, Iustina Alina. "Residential property prices’ modeling: evidence from selected European countries." Journal of European Real Estate Research 9, no. 3 (2016): 273–85. http://dx.doi.org/10.1108/jerer-01-2016-0001.
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Purpose The purpose of this study is to contribute to the relatively narrow existing residential real estate literature by developing and validating several univariate forecasting models, to reliably anticipate future house price dynamics across several European Union (EU) countries. Design/methodology/approach The research approach relies on the time series analysis, by using the Box–Jenkins autoregressive integrated moving average (ARIMA) methodology to explore the trends of residential property prices in selected EU countries and to obtain a snapshot of the potential signs of change to be witnessed by domestic residential markets on a short time-period. The analysis has been performed distinctly for each country in the sample, to account for country-specific past and future trends as well as similarities in their house price growth rate evolutions. The models were estimated for a broad sample of quarterly observations during 1990-2015, while the forecast horizon ranged between the third quarter of 2015 and the fourth quarter of 2016. Findings The findings suggested that residential property prices’ real growth rate can be modeled through the Box–Jenkins method for France, The Netherlands, Sweden and UK. The pattern of Italy’s residential property prices’ real growth rate cannot be explained by means of univariate ARIMA models, being more suited for multivariate models. Originality/value The article subscribes to the need for timely, high-frequency and quality data about house price trends in Europe, to increase the accuracy of forecasts and prevent the appearance of bubbles on real estate market. It compares residential property prices’ dynamics across European countries to identify housing markets with similar patterns of their prices.
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Janbabaee, Ghasem, Aliasghar Nadi-Ghara, Mahdi Afshari, et al. "Forecasting the incidence of breast, colorectal and bladder cancers in north of Iran using time series models; comparing Bayesian, ARIMA and Bootstrap approaches." Asian Pacific Journal of Environment and Cancer 4, no. 1 (2021): 3–7. http://dx.doi.org/10.31557/apjec.2021.4.1.3-7.
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Introduction: Cancers are the second cause of death worldwide. Prevalence and incidence of cancers is getting increased by aging and population growth. This study aims to predict the incidence of breast, colorectal and bladder cancers in north of Iran until 2020 using time series models.
 Methods: The number of breast, colorectal and bladder cancer cases from April 2014 to March 2016 was extracted. The time variable was each month of the study years and using the number of daily registered cancers in each month, the time series of the monthly incident cases was designed. Then, three methods of time series analysis including Box Jenkins, Bayesian and Bootstrap were applied for predicting the incidence of the above cancers until March 2020.
 Results: The number of bladder cancer cases in March 2014 was 6 cases. This study showed that the number of breast cancer cases in March 2020 will be increased to 15, 15 and 26 cases based on ARIMA, Bootstrap and Bayesian methods respectively. In addition, the incident cases of breast cancer, will be increased from 32 in 2014 to 65 (ARIMA method), 47(Bootstrap method) and 364 (Bayesian method). The corresponding figure for colorectal cancer was 30, 30 and 95 respectively.
 Conclusion: The increasing trend of breast, bladder and colorectal cancers will be continued which is considerable based on the Bayesian method results. Considering the limited reliable data used in a short time, it seems that the forecasting results of this model is acceptable.
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Jiang, Yulian, Wuchang Wei, Ramesh Chandra Das, and Tonmoy Chatterjee. "Analysis of the Strategic Emission-Based Energy Policies of Developing and Developed Economies with Twin Prediction Model." Complexity 2020 (November 11, 2020): 1–16. http://dx.doi.org/10.1155/2020/4701678.
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Upholding sustainability in the use of energies for the increasing global industrial activity has been one of the priority agendas of the global leaders of the West and East. The projection of different GHGs has thus been the important policy agenda of the economies to justify the positions of their own as well as of others. Methane is one of the important components of GHGs, and its main sources of generation are the agriculture and livestock activities. Global diplomacy regarding the curtailment of the GHGs has set the target of reducing the levels of GHGs time to time, but the ground reality regarding the reduction is far away from the targets. Sometimes, the targets are fixed without the application of scientific methods. The aim of the present study is to examine sustainability of energy systems through the forecasting of the methane emission and agricultural output of the world’s different income groups up to 2030 using the data for the period 1981–2012. The work is novel in two senses: the existing studies did not use both the Box–Jenkins and artificial neural network methods, and the present study covers all the major economic groups in the world which is unlike to any existing studies. Two methods are used for forecasting of the two. One is the Box–Jenkins method, where linear nature of the two variables is considered and the other is artificial neural network methods where nonlinear nature of the variables is also considered. The results show that, except the OECD group, all the remaining groups display increasing trends of methane emission, but unquestionably, all the groups display increasing trends of agricultural output, where middle- and upper middle-income groups hold the upper berths. The forecasted emission is justified to be sustainable in major groups under both methods of estimations since overall growth of agricultural output is greater than that of methane emission.
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Frenkel’, A. A., N. N. Volkova, A. A. Surkov, and E. I. Romanyuk. "THE APPLICATION OF RIDGE REGRESSION METHODS WHEN COMBINING FORECASTS." Finance: Theory and Practice 22, no. 4 (2018): 6–17. http://dx.doi.org/10.26794/2587-5671-2018-22-4-6-17.
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Forecasting of economic indicators with time series using one or another method or another but the only method leads to the situation that all the information contained in other forecasting methods is usually discarded. The information that is ignored may contain information that allows other features of the economic process to be assessed. Combining forecasts makes possible to take into account almost all the information contained in particular forecasts. In the article, we present the analysis of the application of the method of regression analysis, in particular, ridge regression for finding the weighting coefficients of the particular forecasts in the combined forecast. We compared the accuracy of prediction based on the ridge regression with other methods of combining predictions. The purpose of our research work was an analysis of the most common methods of combining forecasts — various modifications of Granger-Ramanathan methods and comparison with a new approach of combining forecasts based on the ridge regression for its use in practice. We used statistical methods of time series forecasting (the method of harmonic weights, adaptive exponential smoothing using a tracking signal, the method of simple exponential smoothing and the Box-Jenkins model), the method of constructing combined forecasts, as well as methods of regression analysis. As a result, we built the combined forecasts based on annual data for the period from 1950 to 2015 on the production in Russia of some products: steel, metallurgical coke, pulp, plywood, cement. We used the methods of Granger-Ramanathan (without restrictions and with restrictions on the sum of coefficients in partial predictions) and also the ∆-coefficients obtained by the ridge regression method. The forecasts constructed using the Granger-Ramanathan methods give the highest accuracy of the combined forecast. The method based on the ridge regression is less accurate, but better than the separate predictions. At the same time, the proposed method of calculating the weight coefficients on the basis of the ridge regression has a well- developed scheme of calculation and eliminates the negative weight coefficients in the combined forecast.
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Ranganai, Edmore, and Mphiliseni B. Nzuza. "A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data." Journal of Energy in Southern Africa 26, no. 1 (2015): 125–37. http://dx.doi.org/10.17159/2413-3051/2015/v26i1a2215.
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Extra-terrestrially, there is no stochasticity in the solar irradiance, hence deterministic models are often used to model this data. At ground level, the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) short memory stochastic models have been used to model such data with some degree of success. This success is attributable to its ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. However, irradiance data recorded at the earth’s surface is rarely entirely stochastic but a mixture of both deterministic and stochastic components. One plausible modelling procedure is to couple sinusoidal predictors at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle, with SARIMA models capturing the stochastic components. We construct such models which we term, harmonically coupled SARIMA (HCSARIMA) models and use them to empirically model the global horizontal irradiance (GHI) recorded at the earth’s surface. Comparison of the two classes of models shows that HCSARIMA models generally out-compete SARIMA models in the forecasting arena.
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Ribeiro, J., N. Lauzon, J. Rousselle, H. T. Trung, and J. D. Salas. "Comparaison de deux modèles pour la prévision journalière en temps réel des apports naturels." Canadian Journal of Civil Engineering 25, no. 2 (1998): 291–304. http://dx.doi.org/10.1139/l97-099.
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This study presents a comparison of the performances of two models used for real-time forecasting of daily inflows to the Chute-du-Diable and the Lac-Saint-Jean reservoirs, and the daily flows measured on the Mistassibi River basin. The three drainage basins are located in the Saguenay-Lac-Saint-Jean water system. The first model, a conceptual one, is a global deterministic model that is currently being used by Alcan (Aluminum Company of Canada) to predict daily flows in real time. The second model, which forms the primary focus of this study, is based on the structure of models commonly known as "black-box" models, a generalized formulation of the autoregressive moving average models with exogeneous variable of Box and Jenkins (ARMAX). The Kalman filter is coupled with this model to enable day-to-day adjustment of estimated parameters. Autocorrelation and cross-correlation analyses on the data have made it possible to establish the preliminary structure of the black-box model, that is, one per basin. The final structure was chosen following a sensitivity analysis on the parameters. The models retained are all ARMAX models, the statistical behavior of the residuals having demonstrated their adequacy. Comparison of these ARMAX models with Kalman filter and the deterministic model have led to the following conclusion: the ARMAX models with the Kalman filter are superior to the deterministic model for daily prediction in real time within a horizon of 2 days. For a 3-day horizon, the models are, for practical purposes, equivalent. For a horizon of 4 days or more, the deterministic model is superior to the ARMAX models with Kalman filter.Key words: forecasting, black-box models, Kalman filter, deterministic model, natural inflows.
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Lappas, I., and M. Lazaridou. "SEASONAL STOCHASTIC SIMULATION AND TIMESERIES MODELLING - ANALYSIS OF A KARSTIC SPRING IN CENTRAL MACEDONIA, GREECE." Bulletin of the Geological Society of Greece 50, no. 2 (2017): 808. http://dx.doi.org/10.12681/bgsg.11787.
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The objective of this paper is to find an appropriate Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model for fitting the monthly discharge of a karstic spring located at the North of the city of Serres (Agios Ioannis, Mount Menikio) by considering the minimum of Akaike Information Criterion (AIC). Box- Jenkins methodology applies models to find the best fit of a timeseries to past values of this timeseries, in order to make forecasting and consists of a four-step iterative procedure: identification, estimation, diagnostic check and forecasting. Timeseries analysis and forecasting of hydrological parameters such as spring discharge may be useful in decision making and optimum water resources usage. In this study, monthly discharge measurements are analysed. Initial data are firstly transformed to normal and stationary using differencing methods. Autocorrelation and Partial Autocorrelation functions are calculated to determine the order of Autoregressive and Moving Average parameters and residuals are then checked to show the “white noise”. The spring discharge data are forecasted based on the selected model up to 2008 and are then compared with measured values. The timeseries model SARIMA (2,1,1)(1,0,1)12 could be used in monthly discharge forecasting at a short time (upcoming one year) with a simple and explicit model structure in order to help decision m akers to establish priorities in terms of water demand management. Finally, the corr elation coefficient between the observed and fitted data is essentially high, while the absolute and relative errors are significantly low.
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Maswar, Maswar. "ANALISIS TIME SERIES MODEL ARMA UNTUK MEMPREDIKSI JUMLAH SANTRI PP SALAFIYAH SYAFI'IYAH SUKOREJO 2017-2021." LISAN AL-HAL: Jurnal Pengembangan Pemikiran dan Kebudayaan 11, no. 1 (2017): 59–86. http://dx.doi.org/10.35316/lisanalhal.v11i1.177.
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Time series analysis aims to forcasttime seriesdata in some future period based on the data in the past. The main aim of this research is to forcast the number of the new students of Salafiyah Syafi’iyahSukorejo Boarding School in Situbondo using Auto Regressive Moving Average (ARMA). This research uses annual data from 2005 until 2016. It is discusses the steps of timeseriesanlysis using the Box –Jenkinsmethod. That method comprises of several stages, they are model identification stage, parameter estimation stage, diagnostic checking and forecasting stage. Model identification stage is done by finding the model (p,q) that are considered as the most appropriate by looking at the plot of ACF and PACF of the correlogram. Parameter estimation stage is done by estimating model parameters.Whereas, Diagnostic testing and forecasting stage is done by seeing if residual estimation results is already have the quality of white noise.After the appropriate model has been identified, the next step is to use this model for forecasting. The results of this study shows that the ARMA model (2.0) provide the better forecasting results with squared the smallest value of SSR, AICand SIC.
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Waluyo, Jatmiko Edy. "Peramalan Kedatangan Wisatawan Manca Negara Melalui Bandara Husein Sastra Negara Bandung Dengan Menggunakan Metode Arima (Autoregressive Integreted Moving Average)." Jurnal Kepariwisataan: Destinasi, Hospitalitas dan Perjalanan 3, no. 1 (2019): 18–26. http://dx.doi.org/10.34013/jk.v3i1.32.
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Data processing and analysis of foreign tourist arrivals in Bandung through Husein Sastra Bandung Airport is very necessary in an effort to take a decision related to tourism planning in Bandung in particular and national tourism generally, be it planning related to the Airport itself and tourism planning In Bandung Raya. The purpose of this study is to determine the mathematical model or good statistical relationship between the predicted variables (the arrival of foreign tourists through the International Airport Husein Sastra Negara Bandung) with the historical value of these variables using the method of forecasting ARIMA (Autoregressive Integrated Moving Average), so that forecasting can Done with the model. ARIMA is often also called the Box-Jenkins time series method. ARIMA is very good for short-term forecasting, while for long-term forecasting the accuracy of forecasting is not good. Usually will tend to flat (flat / constant) for a long period. The results showed that, to know the accuracy of forecasting model in predicting the data, it can be seen the size of precision of forecasting model in table Fit Model, such as: MAPE, MAE, and others. From the results of fit model testing, it can be seen that the value of MAPE of 21.105% and MAE of 2467.875. This shows that the average accuracy rate of the model in predicting the number of foreign tourist arrivals through Husein Sastra Negara Bandung is 78.895%. To know the value of prediction (prediction) in some period to come, can be seen in table Forecast. While to know the fluctuation of data, either that has happened or will be foreseen. From the forecast table can be known the value of the forecast of tourist arrivals. From the table can also be calculated the estimated maximum error value in forecasting, for example for forecasting in June-December 2017, with 95% confidence level, it is estimated that foreign tourist arrivals will not deviate more than 21.105%.