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Time-series analysis with a hybrid box-jenkins ARIMA

Journal of Harbin Institute of Technology(New Series) 2004年第4期11卷 413-421页

作者： Dilli R Aryal 王要武 School of Management Harbin Institute of Technology Harbin 150001 China

Time-series analysis is important to a wide range of disciplines transcending both the physical and social sciences for proactive policy decisions. Statistical models have sound theoretical basis and have been successfully used in a number of problem domains in time series forecasting. Due to power and flexibility, box-jenkins ARIMA model has gained enormous popularity in many areas and research practice for the last three decades. More recently, the neural networks have been shown to be a promising alternative tool for modeling and forecasting owing to their ability to capture the nonlinearity in the data. However, despite the popularity and the superiority of ARIMA and ANN models, the empirical forecasting performance has been rather mixed so that no single method is best in every situation. In this study, a hybrid ARIMA and neural networks model to time series forecasting is proposed. The basic idea behind the model combination is to use each model’s unique features to capture different patterns in the data. With three real data sets, empirical results evidently show that the hybrid model outperforms ARIMA and ANN model noticeably in terms of forecasting accuracy used in isolation.

关键词： time series analysis ARIMA box-jenkins methodology artificial neural networks hybrid model

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Using box-jenkins Models to Forecast Mobile Cellular Subscription

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Open Journal of Statistics 2016年第2期6卷 303-309页

作者： Ian Siluyele Stanley Jere Zambia Information and Communications Technology Authority (ZICTA) Lusaka Zambia Department of Mathematics and Statistics Mulungushi University Kabwe Zambia

In this paper, the box-jenkins modelling procedure is used to determine an ARIMA model and go further to forecasting. The mobile cellular subscription data for the study were taken from the administrative data submitted to the Zambia Information and Communications Technology Authority (ZICTA) as quarterly returns by all three mobile network operators Airtel Zambia, MTN Zambia and Zamtel. The time series of annual figures for mobile cellular subscription for all mobile network operators is from 2000 to 2014 and has a total of 15 observations. Results show that the ARIMA (1, 2, 1) is an adequate model which best fits the mobile cellular subscription time series and is therefore suitable for forecasting subscription. The model predicts a gradual rise in mobile cellular subscription in the next 5 years, culminating to about 9.0% cumulative increase in 2019.

关键词： Mobile Cellular Subscription box-jenkins methodology ARIMA Model Autocorrelation Function Partial Autocorrelation Function

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Modelling HIV/AIDS Cases in Zambia: A Comparative Study of the Impact of Mandatory HIV Testing

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Open Journal of Statistics 2021年第3期11卷 409-419页

作者： Edwin Moyo James C. Shakalima Gilbert Chambashi James Muchinga Levy K. Matindih Department of Science and Mathematics Mulungushi University Kabwe Zambia School of Business Studies Unicaf University Lusaka Zambia Department of Natural Sciences Levy Mwanawasa University Lusaka Zambia

In this study, a time series modeling approach is used to determine an ARIMA model and advance counterfactual forecasting at a point of policy intervention. We consider monthly data of HIV/AIDS cases from the Ministry of Health (Copperbelt province) of Zambia, for the period 2010 to 2019 and have a total of 120 observations. Results indicate that ARIMA (1, 0, 0) is an adequate model which best fits the HIV/AIDS time series data and is, therefore, suitable for forecasting cases. The model predicts a reduction from an average of 3500 to 3177 representing 14.29% in HIV/AIDS cases from 2017 (year of policy activation) to 2019, but the actual recorded cases dropped from 3500 to 1514 accounting for 57.4% in the same time frame.

关键词： Counterfactual Forecasting box-jenkins methodology ARIMA Model Auto-correlation Function Partial Autocorrelation Function

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