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Proceeding Paper

STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones †

by
Zuokun Ouyang
*,
Philippe Ravier
and
Meryem Jabloun
Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique, Energétique, Université d’Orléans, 12 rue de Blois, 45067 Orléans, France
*
Author to whom correspondence should be addressed.
†
Presented at the 7th International Conference on Time Series and Forecasting, Gran Canaria, Spain, 19–21 July 2021.
Eng. Proc. 2021, 5(1), 42; https://doi.org/10.3390/engproc2021005042
Published: 8 July 2021
(This article belongs to the Proceedings of The 7th International Conference on Time Series and Forecasting)
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Abstract

This paper aims at comparing different forecasting strategies combined with the STL decomposition method. STL is a versatile and robust time series decomposition method. The forecasting strategies we consider are as follows: three statistical methods (ARIMA, ETS, and Theta), five machine learning methods (KNN, SVR, CART, RF, and GP), and two versions of RNNs (CNN-LSTM and ConvLSTM). We conduct the forecasting test on six horizons (1, 6, 12, 18, and 24 months). Our results show that, when applied to monthly industrial M3 Competition data as a preprocessing step, STL decomposition can benefit forecasting using statistical methods but harms the machine learning ones. Moreover, the STL-Theta combination method displays the best forecasting results on four over the five forecasting horizons.
Keywords: time series forecasting; ARIMA; ETS; Theta method; STL decomposition; machine learning; RNN time series forecasting; ARIMA; ETS; Theta method; STL decomposition; machine learning; RNN

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MDPI and ACS Style

Ouyang, Z.; Ravier, P.; Jabloun, M. STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones. Eng. Proc. 2021, 5, 42. https://doi.org/10.3390/engproc2021005042

AMA Style

Ouyang Z, Ravier P, Jabloun M. STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones. Engineering Proceedings. 2021; 5(1):42. https://doi.org/10.3390/engproc2021005042

Chicago/Turabian Style

Ouyang, Zuokun, Philippe Ravier, and Meryem Jabloun. 2021. "STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones" Engineering Proceedings 5, no. 1: 42. https://doi.org/10.3390/engproc2021005042

APA Style

Ouyang, Z., Ravier, P., & Jabloun, M. (2021). STL Decomposition of Time Series Can Benefit Forecasting Done by Statistical Methods but Not by Machine Learning Ones. Engineering Proceedings, 5(1), 42. https://doi.org/10.3390/engproc2021005042

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