Comparison of Linear Regression, ARIMA, Simple Exponential Smoothing, Hybrid ARIMA-LSTM, and EWMA in Forecasting Commodity Prices

Mujiyanto Mujiyanto, Susi Nurindahsari, Rahmafatin Nurul Izza

Abstract


In this study, we compare the performance of both hybrid and non-hybrid forecasting models, explicitly focusing on Linear Regression, ARIMA, Simple Exponential Smoothing, Hybrid ARIMA-LSTM, and EWMA in predicting commodity prices within the volatile market of Central Java, Indonesia. The primary objective is to evaluate which hybrid and non-hybrid models provide the most accurate and reliable forecasts under various conditions. Analyzing daily price data from the SiHaTi platform, an official service provided by Bank Indonesia, the Hybrid ARIMA-LSTM model emerges as the most accurate, achieving a forecast accuracy of 92.5%, compared to the 78.3% and 84.7% accuracies of Linear Regression and ARIMA, respectively. These findings underline the potential advantages of combining machine learning with statistical methods to improve predictions in dynamic market conditions, providing invaluable insights for policymakers and market analysts. However, it should be noted that only one hybrid model was compared, and future research should explore multiple hybrid models to ensure a comprehensive evaluation of their effectiveness.

Keywords


Forecasting Commodity Prices; ARIMA; Simple Exponential Smoothing; Hybrid ARIMA-LSTM; EWMA

Full Text:

Link Download

References


Alhnaity, B., & Abbod, M. (2020). A new hybrid financial time series prediction model. Engineering Applications of Artificial Intelligence, 95. https://doi.org/10.1016/j.engappai.2020.103873

Barhmi, S., Elfatni, O., & Belhaj, I. (2020). Forecasting of wind speed using multiple linear regression and artificial neural networks. Energy Systems, 11(4), 935–946. https://doi.org/10.1007/s12667-019-00338-y

Ben Ameur, H., Boubaker, S., Ftiti, Z., Louhichi, W., & Tissaoui, K. (2023). Forecasting commodity prices: empirical evidence using deep learning tools. Annals of Operations Research. https://doi.org/10.1007/s10479-022-05076-6

Brignoli, P. L., Varacca, A., Gardebroek, C., & Sckokai, P. (2024). Machine learning to predict grains futures prices. Agricultural Economics (United Kingdom), 55(3), 479–497. https://doi.org/10.1111/agec.12828

Chhajer, P., Shah, M., & Kshirsagar, A. (2022). The applications of artificial neural networks, support vector machines, and long–short term memory for stock market prediction. Decision Analytics Journal, 2. https://doi.org/10.1016/j.dajour.2021.100015

Dave, E., Leonardo, A., Jeanice, M., & Hanafiah, N. (2021). Forecasting Indonesia Exports using a Hybrid Model ARIMA-LSTM. Procedia Computer Science, 179, 480–487. https://doi.org/10.1016/j.procs.2021.01.031

Dharavath, R., & Khosla, E. (2019). Seasonal ARIMA to forecast fruits and vegetable agricultural prices. Proceedings - 2019 IEEE International Symposium on Smart Electronic Systems, ISES 2019, 47–52. https://doi.org/10.1109/iSES47678.2019.00023

Guleryuz, D. (2021). Forecasting outbreak of COVID-19 in Turkey; Comparison of Box–Jenkins, Brown’s exponential smoothing and long short-term memory models. Process Safety and Environmental Protection, 149, 927–935. https://doi.org/10.1016/j.psep.2021.03.032

Hakim, A. Q. A., Rahman, M. A. A., & Radzi, S. F. M. (2024). Ensemble ARIMA-LSTM Algorithm in Predicting Agriculture Commodities Market Price for Farmer’s Education. 2024 7th International Conference on Information Technologies in Engineering Education, Inforino 2024 - Proceedings. https://doi.org/10.1109/Inforino60363.2024.10551892

Kahraman, E., & Akay, O. (2023). Comparison of exponential smoothing methods in forecasting global prices of main metals. Mineral Economics, 36(3), 427–435. https://doi.org/10.1007/s13563-022-00354-y

Li, H., Xu, Q., He, Y., Fan, X., & Li, S. (2020). Modeling and predicting reservoir landslide displacement with deep belief network and EWMA control charts: a case study in Three Gorges Reservoir. Landslides, 17(3), 693–707. https://doi.org/10.1007/s10346-019-01312-6

Ma, Q. (2020). Comparison of ARIMA, ANN and LSTM for Stock Price Prediction. E3S Web of Conferences, 218. https://doi.org/10.1051/e3sconf/202021801026

Mohsin, M., & Jamaani, F. (2023). A novel deep-learning technique for forecasting oil price volatility using historical prices of five precious metals in context of green financing – A comparison of deep learning, machine learning, and statistical models. Resources Policy, 86. https://doi.org/10.1016/j.resourpol.2023.104216

Ray, S., Lama, A., Mishra, P., Biswas, T., Sankar Das, S., & Gurung, B. (2023). An ARIMA-LSTM model for predicting volatile agricultural price series with random forest technique [Formula presented]. Applied Soft Computing, 149. https://doi.org/10.1016/j.asoc.2023.110939

Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied Soft Computing Journal, 90. https://doi.org/10.1016/j.asoc.2020.106181

Si, Y., Nadarajah, S., Zhang, Z., & Xu, C. (2024). Modeling opening price spread of Shanghai Composite Index based on ARIMA-GRU/LSTM hybrid model. PLoS ONE, 19(3 March). https://doi.org/10.1371/journal.pone.0299164

Vancsura, L., Tatay, T., & Bareith, T. (2023). Evaluating the Effectiveness of Modern Forecasting Models in Predicting Commodity Futures Prices in Volatile Economic Times. Risks, 11(2). https://doi.org/10.3390/risks11020027

Xidonas, P., Tsionas, M., & Zopounidis, C. (2020). On mutual funds-of-ETFs asset allocation with rebalancing: sample covariance versus EWMA and GARCH. Annals of Operations Research, 284(1), 469–482. https://doi.org/10.1007/s10479-018-3056-z

Yavasani, R., Wang, F., Hills, M., School, H., & Advisor, U. #. (2024). Comparative Analysis of LSTM, GRU, and ARIMA Models for Stock Market Price Prediction. www.JSR.org/hs

Yousefzadeh, M., Hosseini, S. A., & Farnaghi, M. (2021). Spatiotemporally explicit earthquake prediction using deep neural network. Soil Dynamics and Earthquake Engineering, 144. https://doi.org/10.1016/j.soildyn.2021.106663

Zhang, D., Chen, S., Liwen, L., & Xia, Q. (2020). Forecasting Agricultural Commodity Prices Using Model Selection Framework with Time Series Features and Forecast Horizons. IEEE Access, 8, 28197–28209. https://doi.org/10.1109/ACCESS.2020.2971591




DOI: http://dx.doi.org/10.35671/telematika.v17i2.2932

Refbacks



 


Indexed by:

 

Telematika
ISSN: 2442-4528 (online) | ISSN: 1979-925X (print)
Published by : Universitas Amikom Purwokerto
Jl. Let. Jend. POL SUMARTO Watumas, Purwonegoro - Purwokerto, Indonesia


Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License .