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Journal articles on the topic 'Time-series analysis. Box-Jenkins forecasting'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 February 2022

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1

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 Informatio
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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 con
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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 ana
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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
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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 pape
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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
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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 writt
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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 simil
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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
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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 fr
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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 seaso
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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 forecasti
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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. L
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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 autocorrelation
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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 a
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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 t
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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 neura
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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 incide
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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 forecas
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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 insignifican
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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 implemen
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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
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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
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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 app
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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
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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 ut
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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 ro
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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 su
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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 univer
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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
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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 ar
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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 t
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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 integrat
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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
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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 a
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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 medi
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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 mo
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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
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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 w
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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 metho
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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 realit
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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 findin
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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 mixt
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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
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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 forecastin
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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 d
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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 usin
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