DEVELOPING A FORECASTING MODEL TO ENHANCE THE EFFICIENCY OF SUSTAINABLE DEVELOPMENT POLICY: ENRICHING THE LS-ARIMAX MODEL

Authors

  • Pruethsan Sutthichaimethee, Thawatchai Pannarungsee, Surapol Suyaporm, Boonton Dockthaisong, Naiyana Koetwichai มหาวิทยาลัยเวสเทิร์น

Keywords:

LS-ARIMAX model, Sustainable Development, Long term, Short term, Exogenous Variables

Abstract

This study aims to develop a forecasting model by adapting the LS-ARIMAX model in order to enhance the efficiency of sustainable development policy. This unique LS-ARIMAX model is actually developed based on a great forecasting model known as ARIMA Model (p,d,q). AS the LS-ARIMAX model suggests, it comes in full name as Long Term and Short Term- Autocorrelation Integrated Moving Average with Exogeneous and Error Correction Mechanism (LS-ARIMAX model). As of many relevant studies are out in a review, it reflects that LS-ARIMAX model is a newly-developed model designed to support in a policy formulation and planning of Thailand and other countries. This model of LS-ARIMAX is different compared to other existing models and comes with unique key features; deploying a stationary data, integrating with co-integration analysis, considering exogenous variables, implementing ECT to optimize a long-term forecasting capacity and eliminating an issue of Heteroskedasticity, Multicollinearity, and Autocorrelation. Therefore, the above model becomes an ideal and potential forecasting model to be utilized in the process of national policy formulation and planning to achieve sustainability.

Downloads

Download data is not yet available.

References

Achawangkul, Y. Thailand’s Alternative Energy Development Plan, Available online: https://www.unescap.org/sites/default/files/MoE%20_%20AE%20policies.pdf (accessed on 1 August 2018).

Office of the National Economic and Social Development Board (NESDB). Available online: https://www.nesdb.go.th/nesdb_en/more_news.php?cid=154&filename=index (accessed on 1 August 2018).

National Statistic Office Ministry of Information and Communication Technology. Available online: https://web.nso.go.th/index.htm (accessed on 1 August 2018).

Department of Alternative Energy Development and Efficiency. Available online: https://www.dede.go.th/ewtadmin/ewt/dede_web/ewt_news.php?nid=47140 (accessed on 1 August 2018).

Thailand greenhouse gas management organization (public organization). Available online: https://www.tgo.or.th/2015/thai/content.php?s1=7&s2=16&sub3=sub3 (accessed on 1 August 2018).

Zhao, H.R.; Zhao, H.R.; Guo, S. (2016). Using GM (1,1) Optimized by MFO with Rolling Mechanism to Forecast the Electricity Consumption of Inner Mongolia. Appl. Sci., 6, 20.

Li, S.; Li, R. (2017). Comparison of forecasting energy consumption in Shandong, China Using the ARIMA model, GM model, and ARIMA-GM model. Sustainability, 9: 1181.

Xiong, P.P.; Dang, Y.G.; Yao, T.X.; Wang, Z.X. (2014). Optimal modeling and forecasting of the energy consumption and production in China. Energy, 77: 623–634.

Panklib, K.; Prakasvudhisarn, C.; Khummongkol, D. (2015). Electricity Consumption Forecasting in Thailand Using an Artificial Neural Network and Multiple Linear Regression. Energy Sources Part B, 10: 427–434.

Azadeh, A.; Ghaderi, S.F.; Tarverdian, S.; Saberi, M. (2007). Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption. Appl. Math. Comput., 186: 1731–1741.

Günay, M.E. (2016). Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey. Energy Policy, 90: 92–101.

Dai, S.; Niu, D.; Li, Y. (2018). Forecasting of Energy Consumption in China Based on Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm. Appl. Sci., 8: 678.

Wang, Q.; Li, R. (2017). Decline in China’s coal consumption: An evidence of peak coal or a temporary blip. Energy Policy, 108: 696–701.

Suganthi, L.; Samuel, A.A. (2016). Modelling and forecasting energy consumption in INDIA: Influence of socioeconomic variables. Energy Sources Part B Econ. Plan. Policy, 11: 404–411.

Xu, J.; Fleiter, T.; Eichhammer, W.; Fan, Y. (2012). Energy consumption and CO2 emissions in China’s cement industry: A perspective from LMDI decomposition analysis. Energy Policy, 50: 821–832.

Kishita, Y.; Yamaguchi, Y.; Umeda, Y. (2016). Describing Long-Term Electricity Demand Scenarios in the Telecommunications Industry: A Case Study of Japan. Sustainability, 8: 52.

Zhao, W.; Wang, J.; Lu, H. (2014). Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model. Omega, 45: 80–91.

Hamzacebi, C.; Es, H.A. (2014). Forecasting the annual electricity consumption of Turkey using an optimized grey model. Energy, 70: 165–171.

Mu, H.; Dong, X.; Wang, W.; Ning, Y.; Zhou, W. (2002). Improved Gray Forecast Models for China’s Energy Consumption and CO, Emission. J. Desert Res., 22: 142–149.

Zeng, B.; Zhou, M.; Zhang, J. (2017). Forecasting the Energy Consumption of China’s Manufacturing Using a Homologous Grey Prediction Model. Sustainability, 9: 1975.

Jiang, F.; Yang, X.; Li, S. (2018). Comparison of Forecasting India’s Energy Demand Using an MGM, ARIMA Model, MGM-ARIMA Model, and BP Neural Network Model. Sustainability, 10, 7: 2225.

Ediger, V.S.; Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35: 1701–1708.

Ekonomou, L. (201). Greek long-term energy consumption prediction using artificial neural networks. Energy, 35: 512–517.

Ardakani, F.J.; Ardehali, M.M. (2014). Long-term electrical energy consumption forecasting for developing and developed economies based on different optimized models and historical data types. Energy, 65: 452–461.

Supasa, T.; Hsiau, S. S.; Lin, S, M.; Wongsapai, W.; Wu, J. C. (2017). Household Energy Consumption Behaviour for Different Demographic Regions in Thailand from 2000 to 2010. Sustainability, 9: 2328.

Zhao, J.; Thinh, N. X.; Li, C. (2017). Investigation of the Impacts of Urban Land Use Patterns on Energy Consumption in China: A Case Study of 20 Provincial Capital Cities. Sustainability, 9: 1383.

Tian, Y.; Xiong, S.; Ma, X. (2017). Analysis of the Potential Impacts on China’s Industrial Structure in Energy Consumption. Sustainability, 9: 2284.

Ayvaz, B.; Kusakci, A.O. (2017). Electricity consumption forecasting for Turkey with nonhomogeneous discrete grey model. Energy Sources Part B Econ. Plan. Policy, 12: 260–267.

Dickey, D.A.; Fuller, W.A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49: 1057–1072.

Enders, W. (2010). Applied Econometrics Time Series. Wiley Series in Probability and Statistics. University of Alabama: Tuscaloosa, AL, USA.

MacKinnon, J. (1991). Critical Values for Cointegration Test. In Long-Run Economic Relationships; Engle, R., Granger, C., Eds. Oxford University Press: Oxford, UK.

Cryer, J. D.; Chan, K. (2008). Time Series Analysis with Applications in R. 2nd edition. Springer-Verlag: New York.

Johansen, S.; Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration with applications to the demand for money. Oxford Bull. Econ. Statist., 52: 169–210.

Downloads

Published

2019-06-23

Issue

Section

Research Articles