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Journal articles on the topic 'Models arima'

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Published: 4 June 2021

Last updated: 1 February 2022

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1

ALKALI, MUSA ABUBAKAR. "ASSESSING THE FORECASTING PERFORMANCE OF ARIMA AND ARIMAX MODELS OF RESIDENTIAL PRICES IN ABUJA NIGERIA." Asia Proceedings of Social Sciences 4, no. 1 (April 17, 2019): 4–6. http://dx.doi.org/10.31580/apss.v4i1.528.

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This paper compared the out of sample forecasting ability of two Box-Jenkins ARIMA family models: ARIMAX and ARIMA. The forecasting models were tested to forecast real estate residential price in Abuja, Nigeria with quarterly data of average sales of residential price from the first quarter of year 2000 to the last quarter of year 2017. The result shows that the ARIMAX forecasting models, with macroeconomic factors as exogenous variables such as the household income, interest rate, gross domestic products, exchange rate and crude oil price and their lags, provide the best out of sample forecasting models for 2 bedroom, 3 bedroom, 4 bedroom and 5 bedroom, than ARIMA models. Generally, both ARIMA and ARIMAX models are good for short term forecasting modelling.

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2

Marriott, John, and Paul Newbold. "Bayesian Comparison of ARIMA and Stationary ARMA Models." International Statistical Review / Revue Internationale de Statistique 66, no. 3 (December 1998): 323. http://dx.doi.org/10.2307/1403520.

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3

Marriott, John, and Paul Newbold. "Bayesian Comparison of ARIMA and Stationary ARMA Models." International Statistical Review 66, no. 3 (December 1998): 323–36. http://dx.doi.org/10.1111/j.1751-5823.1998.tb00376.x.

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4

Adekanmbi et al.,, Adekanmbi et al ,. "ARIMA and ARIMAX Stochastic Models for Fertility in Nigeria." International Journal of Mathematics and Computer Applications Research 7, no. 5 (2017): 1–20. http://dx.doi.org/10.24247/ijmcaroct20171.

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5

Wang, S., L. L. Liu, L. K. Huang, Y. Z. Yang, and H. Peng. "PERFORMANCE EVALUATION OF IONOSPHERIC TEC FORECASTING MODELS USING GPS OBSERVATIONS AT DIFFERENT LATITUDES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 8, 2020): 1175–82. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-1175-2020.

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Abstract. In this paper, Holt-Winters model, ARMA model and ARIMA model in time series analysis were used to predict total electron content (TEC). Taking ionospheric grid data of quiet period and active period in different longitude and latitude provided by IGS center as sample data, the TEC data of the first 8 days were used to build four kinds of prediction models and forecast TEC values of the next 6 days, and the results were compared with the observations provided by IGS center. The prediction effects of the four models in different ionospheric environments and different longitude and latitude are emphatically analyzed. The experimental results showed that the average relative accuracy of ARMA, ARIMA and Holt-Winters models in the quiet and active ionospheric periods for the prediction of 6 days was 89.85% in the quiet period, and 88.76% in the active period. In both periods, the higher the latitude, the lower the RMS value. In addition, VTEC from IGS center value and ARMA model and ARIMA model and Holt - Winters in the quiet period and active forecast VTEC values were compared, in the quiet period or active, four models of forecasting value can better reflect the spatial and temporal variation characteristics of TEC three latitude, the prediction results of the ARIMA model can better reflect the spatial and temporal variation characteristics; But compared with the active period, the prediction results of calm period are relatively good.

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Dekleva, J., and N. Rožić. "Forecasting: Arima or Kalman Models." IFAC Proceedings Volumes 18, no. 5 (July 1985): 649–56. http://dx.doi.org/10.1016/s1474-6670(17)60634-7.

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7

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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Snyder, Ralph D., J. Keith Ord, and Anne B. Koehler. "Prediction Intervals for ARIMA Models." Journal of Business & Economic Statistics 19, no. 2 (April 2001): 217–25. http://dx.doi.org/10.1198/073500101316970430.

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9

Kumar, Manish, and M. Thenmozhi. "Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models." International Journal of Banking, Accounting and Finance 5, no. 3 (2014): 284. http://dx.doi.org/10.1504/ijbaaf.2014.064307.

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Pektaş, Ali Osman, and H. Kerem Cigizoglu. "ANN hybrid model versus ARIMA and ARIMAX models of runoff coefficient." Journal of Hydrology 500 (September 2013): 21–36. http://dx.doi.org/10.1016/j.jhydrol.2013.07.020.

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Prada-Núñez, Raúl, and Cesar Augusto Hernández-Suárez. "Análisis de una serie de tiempo utilizando diseño de experimentos como herramienta de calibración." Eco matemático 6, no. 1 (January 17, 2015): 50. http://dx.doi.org/10.22463/17948231.459.

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ResumenLas series temporales se usan para estudiar la relación de una variable consigo misma a lo largo del tiempo en intervalos regulares; se consideró el consumo energético de España durante una muestra de 5 días, recurriendo a diversos modelos deterministas se buscaba modelar su comportamiento de la forma más ajustada. Se utiliza el diseño de experimentos para calibrar los parámetros del modelo de HoltWinters validando aquellos efectos que resultan significativos en la minimización del MAPE, con el fin de identificar las Condiciones Operativas Óptimas del modelo. Por último, se evaluan diversos modelos ARIMA aplicados a los residuos obtenidos del modelo de Holt Winters para convertirlo en ruido blanco, utilizando la metodología Box-Jenkins.Palabras claves: modelo Holt-Winters, modelos ARIMA, Series de tiempo. AbstractTime series are used to study the relationship of a variable with itself over time at regular intervals. Energy consumption in Spain was considered for a sample of five days, using various deterministic models sought to model their behavior in the most accurate way. The design of experiments is used to calibrate the model parameters Holt-Winters validating those effects that are significant in minimizing MAPE,in order to identify the optimum operating conditions of the model. Finally, various ARIMA models applied to residues obtained from Holt-Winters model to make it white noise, using the Box-Jenkins methodology are evaluated.Keywords: Holt-Winters model, ARIMA models, Time series.

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12

Gautam, Ratnesh, and Anand K. Sinha. "Time series analysis of reference crop evapotranspiration for Bokaro District, Jharkhand, India." Journal of Water and Land Development 30, no. 1 (September 1, 2016): 51–56. http://dx.doi.org/10.1515/jwld-2016-0021.

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AbstractEvapotranspiration is the one of the major role playing element in water cycle. More accurate measurement and forecasting of Evapotranspiration would enable more efficient water resources management. This study, is therefore, particularly focused on evapotranspiration modelling and forecasting, since forecasting would provide better information for optimal water resources management. There are numerous techniques of evapotranspiration forecasting that include autoregressive (AR) and moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), Thomas Feiring, etc. Out of these models ARIMA model has been found to be more suitable for analysis and forecasting of hydrological events. Therefore, in this study ARIMA models have been used for forecasting of mean monthly reference crop evapotranspiration by stochastic analysis. The data series of 102 years i.e. 1224 months of Bokaro District were used for analysis and forecasting. Different order of ARIMA model was selected on the basis of autocorrelation function (ACF) and partial autocorrelation (PACF) of data series. Maximum likelihood method was used for determining the parameters of the models. To see the statistical parameter of model, best fitted model is ARIMA (0, 1, 4) (0, 1, 1)12.

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13

Ip, W. H. "Rule-based ARIMA models for FMS." Journal of Materials Processing Technology 66, no. 1-3 (April 1997): 240–43. http://dx.doi.org/10.1016/s0924-0136(96)02531-9.

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14

Cryer, Jonathan D., John C. Nankervis, and N. E. Savin. "Forecast Error Symmetry in ARIMA Models." Journal of the American Statistical Association 85, no. 411 (September 1990): 724–28. http://dx.doi.org/10.1080/01621459.1990.10474933.

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15

Abonazel, Mohamed Reda, and Ahmed Ibrahim Abd-Elftah. "Forecasting Egyptian GDP using ARIMA models." Reports on Economics and Finance 5, no. 1 (2019): 35–47. http://dx.doi.org/10.12988/ref.2019.81023.

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16

An, Hongzhi, and Hongye Gao. "Two limit theorems on ARIMA models." Acta Mathematicae Applicatae Sinica 4, no. 2 (May 1988): 154–64. http://dx.doi.org/10.1007/bf02006064.

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17

Zhang, Manfei, Yimeng Wang, Xiao Wang, and Weibo Zhou. "Groundwater Depth Forecasting Using a Coupled Model." Discrete Dynamics in Nature and Society 2021 (February 24, 2021): 1–11. http://dx.doi.org/10.1155/2021/6614195.

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Accurate and reliable prediction of groundwater depth is a critical component in water resources management. In this paper, a new method based on coupling wavelet decomposition method (WA), autoregressive moving average (ARMA) model, and BP neural network (BP) model for groundwater depth forecasting applications was proposed. The relative performance of the proposed coupled model (WA-ARMA-BP) was compared to the regular autoregressive integrated moving average (ARIMA) and BP models for annual average groundwater depth forecasting using leave-one-out cross-validation (LOO-CV). The variables used to develop and validate the models were average groundwater depth data recorded from 1981 to 2010 in Jinghui Canal Irrigation District in the northwest of China. It was found that the WA-ARMA-BP model provided more accurate annual average groundwater depth forecasts compared to the ARIMA and BP models. The results of the study indicate the potential of the WA-ARMA-BP model in forecasting nonstationary time series such as groundwater depth.

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18

Mah, P. J. W., N. A. M. Ihwal, and N. Z. Azizan. "FORECASTING FRESH WATER AND MARINE FISH PRODUCTION IN MALAYSIA USING ARIMA AND ARFIMA MODELS." MALAYSIAN JOURNAL OF COMPUTING 3, no. 2 (December 31, 2018): 81. http://dx.doi.org/10.24191/mjoc.v3i2.4887.

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Malaysia is surrounded by sea, rivers and lakes which provide natural sources of fish for human consumption. Hence, fish is one source of protein supply to the country and fishery is a sub-sector that contribute to the national gross domestic product. Since fish forecasting is crucial in fisheries management for managers and scientists, time series modelling can be one useful tool. Time series modelling have been used in many fields of studies including the fields of fisheries. In a previous research, the ARIMA and ARFIMA models were used to model marine fish production in Malaysia and the ARFIMA model emerged to be a better forecast model. In this study, we consider fitting the ARIMA and ARFIMA to both the marine and freshwater fish production in Malaysia. The process of model fitting was done using the “ITSM 2000, version 7.0” software. The performance of the models were evaluated using the mean absolute error, root mean square error and mean absolute percentage error. It was found in this study that the selection of the best fit model depends on the forecast accuracy measures used.

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Putera, Muhammad Luthfi Setiarno. "IMPROVISASI MODEL ARIMAX-ANFIS DENGAN VARIASI KALENDER UNTUK PREDIKSI TOTAL TRANSAKSI NON-TUNAI." Indonesian Journal of Statistics and Its Applications 4, no. 2 (July 31, 2020): 296–310. http://dx.doi.org/10.29244/ijsa.v4i2.603.

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Developed information technology boosts interest to use non-cash payment media in many areas. Following the high usage of a non-cash scheme in many payment transactions recently, the objective of this work is two-fold that is to predict the total of a non-cash transaction by using various time-series models and to compare the forecasting accuracy of those models. As a country with a mostly dense Moslem population, plenty of economical activities are arguably influenced by the Islamic calendar effect. Therefore the models being compared are ARIMA, ARIMA with Exogenous (ARIMAX), and a hybrid between ARIMAX and Adaptive Neuro-Fuzzy Inference Systems (ANFIS). By taking such calendar variation into account, the result shows that ARIMAX-ANFIS is the best method in predicting non-cash transactions since it produces lower MAPE. It is indicated that non-cash transaction increases significantly ahead of Ied Fitr occurrence and hits the peak in December. It demonstrates that the hybrid model can improve the accuracy performance of prediction.

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20

Ilie, Ovidiu-Dumitru, Roxana-Oana Cojocariu, Alin Ciobica, Sergiu-Ioan Timofte, Ioannis Mavroudis, and Bogdan Doroftei. "Forecasting the Spreading of COVID-19 across Nine Countries from Europe, Asia, and the American Continents Using the ARIMA Models." Microorganisms 8, no. 8 (July 30, 2020): 1158. http://dx.doi.org/10.3390/microorganisms8081158.

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Since mid-November 2019, when the first SARS-CoV-2-infected patient was officially reported, the new coronavirus has affected over 10 million people from which half a million died during this short period. There is an urgent need to monitor, predict, and restrict COVID-19 in a more efficient manner. This is why Auto-Regressive Integrated Moving Average (ARIMA) models have been developed and used to predict the epidemiological trend of COVID-19 in Ukraine, Romania, the Republic of Moldova, Serbia, Bulgaria, Hungary, USA, Brazil, and India, these last three countries being otherwise the most affected presently. To increase accuracy, the daily prevalence data of COVID-19 from 10 March 2020 to 10 July 2020 were collected from the official website of the Romanian Government GOV.RO, World Health Organization (WHO), and European Centre for Disease Prevention and Control (ECDC) websites. Several ARIMA models were formulated with different ARIMA parameters. ARIMA (1, 1, 0), ARIMA (3, 2, 2), ARIMA (3, 2, 2), ARIMA (3, 1, 1), ARIMA (1, 0, 3), ARIMA (1, 2, 0), ARIMA (1, 1, 0), ARIMA (0, 2, 1), and ARIMA (0, 2, 0) models were chosen as the best models, depending on their lowest Mean Absolute Percentage Error (MAPE) values for Ukraine, Romania, the Republic of Moldova, Serbia, Bulgaria, Hungary, USA, Brazil, and India (4.70244, 1.40016, 2.76751, 2.16733, 2.98154, 2.11239, 3.21569, 4.10596, 2.78051). This study demonstrates that ARIMA models are suitable for making predictions during the current crisis and offers an idea of the epidemiological stage of these regions.

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Mahmad Azan, Atiqa Nur Azza, Nur Faizatul Auni Mohd Zulkifly Mototo, and Pauline Jin Wee Mah. "The Comparison between ARIMA and ARFIMA Model to Forecast Kijang Emas (Gold) Prices in Malaysia using MAE, RMSE and MAPE." Journal of Computing Research and Innovation 6, no. 3 (September 13, 2021): 22–33. http://dx.doi.org/10.24191/jcrinn.v6i3.225.

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Gold is known as the most valuable commodity in the world because it is a universal currency recognized by every single bank across the globe. Thus, many people were interested in investing gold since gold market was always steadier compared to other investment (Khamis and Awang, 2020). However, the credibility of gold was questionable due to the changes in gold prices caused by a variety of circumstances (Henriksen, 2018). Hence, information on the inflation of gold prices were needed to understand the trend in order to plan for the future in accordance with international gold price standards. The aim of this study was to identify the trend of Kijang Emas monthly average prices in Malaysia from the year 2010 to 2021, to determine the best fit time series model for Kijang Emas prices in Malaysia and using univariate time series models to forecast Kijang Emas prices in Malaysia. The ARIMA and ARFIMA models were used in this study to model and forecast the prices of gold (Kijang Emas) in Malaysia. Each of the actual monthly Kijang Emas prices for 2021 were found to be within the 95% predicted intervals for both the ARIMA and ARFIMA models. The performances for each model were checked by considering the values of MAE, RMSE and MAPE. From the findings, all the MAE, RMSE and MAPE values showed that the ARFIMA model emerged as the better model in forecasting the Kijang Emas prices in Malaysia compared to the ARIMA model.

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Derbentsev, Vasily, Natalia Datsenko, Olga Stepanenko, and Vitaly Bezkorovainyi. "Forecasting cryptocurrency prices time series using machine learning approach." SHS Web of Conferences 65 (2019): 02001. http://dx.doi.org/10.1051/shsconf/20196502001.

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This paper describes the construction of the short-term forecasting model of cryptocurrencies’ prices using machine learning approach. The modified model of Binary Auto Regressive Tree (BART) is adapted from the standard models of regression trees and the data of the time series. BART combines the classic algorithm classification and regression trees (C&RT) and autoregressive models ARIMA. Using the BART model, we made a short-term forecast (from 5 to 30 days) for the 3 most capitalized cryptocurrencies: Bitcoin, Ethereum and Ripple. We found that the proposed approach was more accurate than the ARIMA-ARFIMA models in forecasting cryptocurrencies time series both in the periods of slow rising (falling) and in the periods of transition dynamics (change of trend).

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Yu, Zhiheng, Tieli Sun, Hongguang Sun, and Fengqin Yang. "Research on Combinational Forecast Models for the Traffic Flow." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/201686.

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In order to improve the prediction accuracy of the traffic flow, this paper proposes two combinational forecast models based on GM, ARIMA, and GRNN. Firstly, the paper proposes the concept of associate-forecast and the weight distribution method based on reciprocal absolute percentage error and then uses GM(1,1), ARIMA, and GRNN to establish a combinational model of highway traffic flow according to the fixed weight coefficients. Then the paper proposes the use of neural networks to determine variable weight coefficients and establishes Elman combinational forecast model based on GM(1,1), ARIMA, and GRNN, which achieves the integration of these three individuals. Lastly, these two combinational models are applied to highway traffic flow on Chongzun of China and the experimental results verify their effectiveness compared with GM(1,1), ARIMA, and GRNN.

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24

Zhang, Rui, Zhen Guo, Yujie Meng, Songwang Wang, Shaoqiong Li, Ran Niu, Yu Wang, Qing Guo, and Yonghong Li. "Comparison of ARIMA and LSTM in Forecasting the Incidence of HFMD Combined and Uncombined with Exogenous Meteorological Variables in Ningbo, China." International Journal of Environmental Research and Public Health 18, no. 11 (June 7, 2021): 6174. http://dx.doi.org/10.3390/ijerph18116174.

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Background: This study intends to identify the best model for predicting the incidence of hand, foot and mouth disease (HFMD) in Ningbo by comparing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) models combined and uncombined with exogenous meteorological variables. Methods: The data of daily HFMD incidence in Ningbo from January 2014 to November 2017 were set as the training set, and the data of December 2017 were set as the test set. ARIMA and LSTM models combined and uncombined with exogenous meteorological variables were adopted to fit the daily incidence of HFMD by using the data of the training set. The forecasting performances of the four fitted models were verified by using the data of the test set. Root mean square error (RMSE) was selected as the main measure to evaluate the performance of the models. Results: The RMSE for multivariate LSTM, univariate LSTM, ARIMA and ARIMAX (Autoregressive Integrated Moving Average Model with Exogenous Input Variables) was 10.78, 11.20, 12.43 and 14.73, respectively. The LSTM model with exogenous meteorological variables has the best performance among the four models and meteorological variables can increase the prediction accuracy of LSTM model. For the ARIMA model, exogenous meteorological variables did not increase the prediction accuracy but became the interference factor of the model. Conclusions: Multivariate LSTM is the best among the four models to fit the daily incidence of HFMD in Ningbo. It can provide a scientific method to build the HFMD early warning system and the methodology can also be applied to other communicable diseases.

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Zhang, Xiaofan, Chao Liu, and Yuhang Qian. "Coal Price Forecast Based on ARIMA Model." Financial Forum 9, no. 4 (January 28, 2021): 180. http://dx.doi.org/10.18282/ff.v9i4.1530.

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<div>This paper analyzes and determines the decision variables and constraints, establishes the EECM-ARAMA model to analyze and research coal price forecasts. Firstly, we first confirm the influencing factors. Then, we conduct correlation coefficient tests on price and various factors, and get the strength of the correlation between each factor and price. The second is to establish a coal price prediction model. Firstly, we use the EEMD method to transform the original price series into a stable time series, and then formulate three ARIMA models by comparing the size of the influencing factors and the parameter estimation results. After testing, we finally choose the ARIMA 03 model to predict the next 31 days, 35 Weekly and 36-month coal prices. Finally, we combine the models and ideas of the above issues to obtain factors that affect coal price changes and related price prediction models, and combine experience to give some feasible policy recommendations.</div>

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Obi, C. V., and C. N. Okoli. "Comparative Performance of the ARIMA, ARIMAX and SES Model for Estimating Reported Cases of Diabetes Mellitus in Anambra State, Nigeria." European Journal of Engineering and Technology Research 6, no. 1 (January 12, 2021): 63–68. http://dx.doi.org/10.24018/ejers.2021.6.1.2321.

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This study examined the performance of the ARIMA, ARIMAX and the Single Exponential Smoothing (SES) model for the estimation of diabetes cases in Anambra State with the following specific objectives: to fit the model to the data, to determine the best fit model for estimating diabetes mellitus cases and forecast for expected cases for period of five years. The secondary data used for the study is sourced from records of Anambra state Ministry of Health. The Akaike information criterion is adopted for assessing the performance of the models. The R-software is employed for the analysis of data. The results obtained showed that the data satisfied normality and stationarity requirements. The finding of the study showed that ARIMA model has least value of AIC of 1177.92, following the ARIMAX model with value of AIC=1542.25 and SEM recorded highest value of 1595.67. The findings further revealed that the ARIMA has the least values across the measures of accuracy. More so, five years predictions of the cases of diabetes mellitus were obtained using the models under study. From the results of the findings, ARIMA model proved to be best alternative for estimating reported cases of diabetes mellitus in Anambra state. Based on the findings, we recommend there is need for medical practitioners /health planners to create awareness and inform patients about the possible related risk factors of death through early diagnosis and intervention.

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Papamichail, Dimitris M., and Pantazis E. Georgiou. "SEASONAL ARIMA INFLOW MODELS FOR RESERVOIR SIZING1." JAWRA Journal of the American Water Resources Association 37, no. 4 (August 2001): 877–85. http://dx.doi.org/10.1111/j.1752-1688.2001.tb05519.x.

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McGough, Tony, and Sotiris Tsolacos. "Forecasting commercial rental values using ARIMA models." Journal of Property Valuation and Investment 13, no. 5 (December 1995): 6–22. http://dx.doi.org/10.1108/14635789510147801.

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29

Heiberger, Richard M., and Paulo Teles. "Displays for Direct Comparison of ARIMA Models." American Statistician 56, no. 2 (May 2002): 131–38. http://dx.doi.org/10.1198/000313002317572808.

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30

Saez, M. "Letter. ARIMA models in public health surveillance." European Journal of Public Health 9, no. 1 (March 1, 1999): 68. http://dx.doi.org/10.1093/eurpub/9.1.68.

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Peiris, M. S., and B. J. C. Perera. "ON PREDICTION WITH FRACTIONALLY DIFFERENCED ARIMA MODELS." Journal of Time Series Analysis 9, no. 3 (May 1988): 215–20. http://dx.doi.org/10.1111/j.1467-9892.1988.tb00465.x.

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32

Piccolo, Domenico. "A DISTANCE MEASURE FOR CLASSIFYING ARIMA MODELS." Journal of Time Series Analysis 11, no. 2 (March 1990): 153–64. http://dx.doi.org/10.1111/j.1467-9892.1990.tb00048.x.

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Lee, Sangyeol, Siyun Park, Koichi Maekawa, and Ken-Ichi Kawai. "Test for Parameter Change in ARIMA Models." Communications in Statistics - Simulation and Computation 35, no. 2 (July 2006): 429–39. http://dx.doi.org/10.1080/03610910600591537.

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34

Lin, Bin-Shan, Doris Layton MacKenzie, and Thomas R. Gulledge. "Using ARIMA models to predict prison populations." Journal of Quantitative Criminology 2, no. 3 (September 1986): 251–64. http://dx.doi.org/10.1007/bf01066529.

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35

Khoshgoftaar, Taghi M., and Robert M. Szabo. "Investigating ARIMA models of software system quality." Software Quality Journal 4, no. 1 (March 1995): 33–48. http://dx.doi.org/10.1007/bf00404648.

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36

Abeysinghe, Tilak, Uditha Balasooriya, and Albert Tsui. "Small-Sample Forecasting Regression or Arima Models?" Journal of Quantitative Economics 1, no. 1 (January 2003): 103–13. http://dx.doi.org/10.1007/bf03404652.

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Pitaloka, Riski Arum, Sugito Sugito, and Rita Rahmawati. "PERBANDINGAN METODE ARIMA BOX-JENKINS DENGAN ARIMA ENSEMBLE PADA PERAMALAN NILAI IMPOR PROVINSI JAWA TENGAH." Jurnal Gaussian 8, no. 2 (May 30, 2019): 194–207. http://dx.doi.org/10.14710/j.gauss.v8i2.26648.

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Import is activities to enter goods into the territory of a country, both commercial and non-commercial include goods that will be processed domestically. Import is an important requirement for industry in Central Java. The increase in high import values can cause deficit in the trade balance. Appropriate information about the projected amount of imports is needed so that the government can anticipate a high increase in imports through several policies that can be done. The forecasting method that can be used is ARIMA Box-Jenkins. The development of modeling in the field of time series forecasting shows that forecasting accuracy increases if it results from the merging of several models called ensemble ARIMA. The ensemble method used is averaging and stacking. The data used are monthly import value data in Central Java from January 2010 to December 2018. Modeling time series with Box-Jenkins ARIMA produces two significant models, namely ARIMA (2,1,0) and ARIMA (0,1,1). Both models are combined using the ARIMA ensemble averaging and stacking method. The best model chosen from the ARIMA method and ensemble ARIMA based on the least RMSE value is the ARIMA model (2,1,0) with RMSE value of 185,8892 Keywords: Import, ARIMA, ARIMA Ensemble, Stacking, Averaging

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Adebiyi, Ayodele Ariyo, Aderemi Oluyinka Adewumi, and Charles Korede Ayo. "Comparison of ARIMA and Artificial Neural Networks Models for Stock Price Prediction." Journal of Applied Mathematics 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/614342.

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This paper examines the forecasting performance of ARIMA and artificial neural networks model with published stock data obtained from New York Stock Exchange. The empirical results obtained reveal the superiority of neural networks model over ARIMA model. The findings further resolve and clarify contradictory opinions reported in literature over the superiority of neural networks and ARIMA model and vice versa.

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Masood, M. Asif, Irum Raza, and Saleem Abid. "Forecasting Wheat Production Using Time Series Models in Pakistan." Asian Journal of Agriculture and Rural Development 8, no. 2 (February 8, 2019): 172–77. http://dx.doi.org/10.18488/journal.1005/2018.8.2/1005.2.172.177.

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The present paper was designed to forecast wheat production for 2017-18, 2018-19 and 2019-2020 respectively by using time series data from 1971-72 to 2016-17 with best selected time series models. Linear, Quadratic, Exponential, S-Curve, Double Exponential Smoothing, Single exponential smoothing, Moving average and ARIMA were estimated for wheat production. The results showed a mix trend in production of wheat for selected time period. ARIMA (2,1,2) was found best one keeping in view close forecasts with actual reported wheat production. So the preference inclined towards the ARIMA (2,1,2) than quadratic to forecasts of wheat production.

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Eissa, Noura. "Forecasting the GDP per Capita for Egypt and Saudi Arabia Using ARIMA Models." Research in World Economy 11, no. 1 (March 30, 2020): 247. http://dx.doi.org/10.5430/rwe.v11n1p247.

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Annual time series data is used to forecast GDP per capita using the Box-Jenkins Autoregressive-Integrated Moving-Average (ARIMA) model for the Egyptian and Saudi Arabian economies. The fitted ARIMA model is tested for per capita GDP forecasting of Egypt and of Saudi Arabia for the next ten years. Conclusions convey that the most accurate statistical model as in previous literature that forecast GDP per capita for Egypt and for Saudi Arabia is ARIMA (1,1,2) and ARIMA (1,1,1) respectively. The diagnostic tests reveal that the two models presented individually are both stable and reliable.

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Mah, Pauline Jin Wee, and Nur Nadhirah Nanyan. "A COMPARATIVE STUDY BETWEEN UNIVARIATE AND BIVARIATE TIME SERIES MODELS FOR CRUDE PALM OIL INDUSTRY IN PENINSULAR MALAYSIA." MALAYSIAN JOURNAL OF COMPUTING 5, no. 1 (March 6, 2020): 374. http://dx.doi.org/10.24191/mjoc.v5i1.6760.

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The main purpose of this study is to compare the performances of univariate and bivariate models on four time series variables of the crude palm oil industry in Peninsular Malaysia. The monthly data for the four variables, which are the crude palm oil production, price, import and export, were obtained from Malaysian Palm Oil Board (MPOB) and Malaysian Palm Oil Council (MPOC). In the first part of this study, univariate time series models, namely, the autoregressive integrated moving average (ARIMA), fractionally integrated autoregressive moving average (ARFIMA) and autoregressive autoregressive (ARAR) algorithm were used for modelling and forecasting purposes. Subsequently, the dependence between any two of the four variables were checked using the residuals’ sample cross correlation functions before modelling the bivariate time series. In order to model the bivariate time series and make prediction, the transfer function models were used. The forecast accuracy criteria used to evaluate the performances of the models were the mean absolute error (MAE), root mean square error (RMSE) and mean absolute percentage error (MAPE). The results of the univariate time series showed that the best model for predicting the production was ARIMA while the ARAR algorithm were the best forecast models for predicting both the import and export of crude palm oil. However, ARIMA appeared to be the best forecast model for price based on the MAE and MAPE values while ARFIMA emerged the best model based on the RMSE value. When considering bivariate time series models, the production was dependent on import while the export was dependent on either price or import. The results showed that the bivariate models had better performance compared to the univariate models for production and export of crude palm oil based on the forecast accuracy criteria used.

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Sultana, Abira, and Murshida Khanam. "Forecasting Rice Production of Bangladesh Using ARIMA and Artificial Neural Network Models." Dhaka University Journal of Science 68, no. 2 (October 29, 2020): 143–47. http://dx.doi.org/10.3329/dujs.v68i2.54612.

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Forecasting behavior of Econometric and Machine Learning models has recently attracted much attention in the research sector. In this study an attempt has been made to compare the forecasting behavior of Autoregressive Integrated Moving Average (ARIMA) and Artificial Neural Network (ANN) using univariate time series data of annual rice production (1972 to 2013) of Bangladesh. Here, suitable ARIMA has been chosen from several selected ARIMA models with the help of AIC and BIC values. A simple ANN model using backpropagation algorithm with appropriate number of nodes or neurons in a single hidden layer, adjustable threshold value and learning rate, has been constructed. Based on the RMSE, MAE and MAPE values, the results showed that the estimated error of ANN is much higher than the estimated error of chosen ARIMA. So, according to this study, it can be said that the ARIMA model is better than ANN model for forecasting the rice production in Bangladesh. Dhaka Univ. J. Sci. 68(2): 143-147, 2020 (July)

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Jadevicius, Arvydas, and Simon Huston. "Property market modelling and forecasting: simple vs complex models." Journal of Property Investment & Finance 33, no. 4 (July 6, 2015): 337–61. http://dx.doi.org/10.1108/jpif-08-2014-0053.

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Purpose – The commercial property market is complex, but the literature suggests that simple models can forecast it. To confirm the claim, the purpose of this paper is to assess a set of models to forecast UK commercial property market. Design/methodology/approach – The employs five modelling techniques, including Autoregressive Integrated Moving Average (ARIMA), ARIMA with a vector of an explanatory variable(s) (ARIMAX), Simple Regression (SR), Multiple Regression, and Vector Autoregression (VAR) to model IPD UK All Property Rents Index. The Bank Rate, Construction Orders, Employment, Expenditure, FTSE AS Index, Gross Domestic Product (GDP), and Inflation are all explanatory variables selected for the research. Findings – The modelling results confirm that increased model complexity does not necessarily yield greater forecasting accuracy. The analysis shows that although the more complex VAR specification is amongst the best fitting models, its accuracy in producing out-of-sample forecasts is poorer than of some less complex specifications. The average Theil’s U-value for VAR model is around 0.65, which is higher than that of less complex SR with Expenditure (0.176) or ARIMAX (3,0,3) with GDP (0.31) as an explanatory variable models. Practical implications – The paper calls analysts to make forecasts more user-friendly, which are easy to use or understand, and for researchers to pay greater attention to the development and improvement of simpler forecasting techniques or simplification of more complex structures. Originality/value – The paper addresses the issue of complexity in modelling commercial property market. It advocates for simplicity in modelling and forecasting.

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Makwinja, Rodgers, Seyoum Mengistou, Emmanuel Kaunda, Tena Alemiew, Titus Bandulo Phiri, Ishmael Bobby Mphangwe Kosamu, and Chikumbusko Chiziwa Kaonga. "Modeling of Lake Malombe Annual Fish Landings and Catch per Unit Effort (CPUE)." Forecasting 3, no. 1 (February 8, 2021): 39–55. http://dx.doi.org/10.3390/forecast3010004.

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Forecasting, using time series data, has become the most relevant and effective tool for fisheries stock assessment. Autoregressive integrated moving average (ARIMA) modeling has been commonly used to predict the general trend for fish landings with increased reliability and precision. In this paper, ARIMA models were applied to predict Lake Malombe annual fish landings and catch per unit effort (CPUE). The annual fish landings and CPUE trends were first observed and both were non-stationary. The first-order differencing was applied to transform the non-stationary data into stationary. Autocorrelation functions (AC), partial autocorrelation function (PAC), Akaike information criterion (AIC), Bayesian information criterion (BIC), square root of the mean square error (RMSE), the mean absolute error (MAE), percentage standard error of prediction (SEP), average relative variance (ARV), Gaussian maximum likelihood estimation (GMLE) algorithm, efficiency coefficient (E2), coefficient of determination (R2), and persistent index (PI) were estimated, which led to the identification and construction of ARIMA models, suitable in explaining the time series and forecasting. According to the measures of forecasting accuracy, the best forecasting models for fish landings and CPUE were ARIMA (0,1,1) and ARIMA (0,1,0). These models had the lowest values AIC, BIC, RMSE, MAE, SEP, ARV. The models further displayed the highest values of GMLE, PI, R2, and E2. The “auto. arima ()” command in R version 3.6.3 further displayed ARIMA (0,1,1) and ARIMA (0,1,0) as the best. The selected models satisfactorily forecasted the fish landings of 2725.243 metric tons and CPUE of 0.097 kg/h by 2024.

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Seyedi, Seyed Navid, Pouyan Rezvan, Saeed Akbarnatajbisheh, and Syed Ahmad Helmi. "Evaluating ARIMA-Neural Network Hybrid Model Performance in Forecasting Stationary Timeseries." Advanced Materials Research 845 (December 2013): 510–15. http://dx.doi.org/10.4028/www.scientific.net/amr.845.510.

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Demand prediction is one of most sophisticated steps in planning and investments. Although many studies are conducted to find the appropriate forecasting models, dynamic nature of forecasted parameters and their effecting factors are apparent evidences for continuous researches. ARIMA, Artificial Neural Network (ANN), and ARIMA-ANN hybrid model are well-known forecasting models. Many researchers concluded that the Hybrid model is the predominant forecasting model in comparison with ARIMA and ANN individual models. Most of these researches are based on non-stationary or seasonal timeseries, whereas in this article, hybrid models forecast ability by stationary time series is studied. Some following demand time steps from a paint manufacturing company are forecasted by all previously mentioned models and ARIMA-ANN hybrid model fails to present the best forecasts.

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Musa, Mohammed Ibrahim. "Malaria Disease Distribution in Sudan Using Time Series ARIMA Model." International Journal of Public Health Science (IJPHS) 4, no. 1 (March 1, 2015): 7. http://dx.doi.org/10.11591/ijphs.v4i1.4705.

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<p>Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)<sup>12</sup>. The ARIMAX model showed that there is a significant variation between the states in Sudan.</p>

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Musa, Mohammed Ibrahim. "Malaria Disease Distribution in Sudan Using Time Series ARIMA Model." International Journal of Public Health Science (IJPHS) 4, no. 1 (March 1, 2015): 7. http://dx.doi.org/10.11591/.v4i1.4705.

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<p>Malaria is widely spread and distributed in the tropical and subtropical regions of the world. Sudan is a sub-Saharan African country that is highly affected by malaria with 7.5 million cases and 35,000 deaths every year. The auto-regressive integrated moving average (ARIMA) model was used to predict the spread of malaria in the Sudan. The ARIMA model used malaria cases from 2006 to 2011 as a training set, and data from 2012 as a testing set, and created the best model fitted to forecast the malaria cases in Sudan for years 2013 and 2014. The ARIMAX model was carried out to examine the relationship between malaria cases and climate factors with diagnostics of previous malaria cases using the least Bayesian Information Criteria (BIC) values. The results indicated that there were four different models, the ARIMA model of the average for the overall states is (1,0,1)(0,1,1)<sup>12</sup>. The ARIMAX model showed that there is a significant variation between the states in Sudan.</p>

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Dawoud, Issam, and Selahattin Kaçiranlar. "An optimal k of kth MA-ARIMA models under a class of ARIMA model." Communications in Statistics - Theory and Methods 46, no. 12 (June 9, 2016): 5754–65. http://dx.doi.org/10.1080/03610926.2015.1112910.

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Pitfield, D. E. "Predicting Air-Transport Demand." Environment and Planning A: Economy and Space 25, no. 4 (April 1993): 459–66. http://dx.doi.org/10.1068/a250459.

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In this paper, the efficiency of autoregressive integrated moving average (ARIMA) and regression models in simulating air-transport passengers by route are compared and constrasted. It is concluded that ARIMA models are far superior not only in their simulation capabilities but also in their applicability to such data. In the context of the UK Civil Aviation Authority's approach to forecasting, it is suggested that ARIMA models, including those with intervention terms, bear closer examination.

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Dong, Ming Ke, Chen Chen, Min Hua Huang, and Ye Jin. "Joint Network Traffic Forecast with ARIMA Models and Chaotic Models Based on Wavelet Analysis." Applied Mechanics and Materials 55-57 (May 2011): 743–46. http://dx.doi.org/10.4028/www.scientific.net/amm.55-57.743.

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In the recent study of network traffic, it is shown that the traffic flow presents both periodic and self-similar characteristics. Due to these two features, the short-term forecast of network traffic cannot be accurately fit in either autoregressive integrated moving average (ARIMA) models which is suitable for linear behavior, or chaotic models which is corresponding to self-similarity characteristic. In this paper, our methodology suggests that by using wavelet multiresolution analysis, we can obtain a joint short-term network traffic prediction method and get a more precise forecast result as compared to using either ARIMA models or chaotic models. We also run simulations to show the improvement of prediction accuracy of our proposed approach.

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