Open AccessComparative Study of Short-Term Forecasting Methods for Soybean Oil Futures Based on LSTM, SVR, ES and Wavelet Transformation
Author(s) -
Ganqiong Li,
Wei Chen,
Denghua Li,
Dongjie Wang,
Shiwei Xu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1682/1/012007
Subject(s) - exponential smoothing , wavelet , transformation (genetics) , futures contract , computer science , term (time) , series (stratigraphy) , smoothing , time series , artificial intelligence , econometrics , machine learning , mathematics , finance , economics , paleontology , biochemistry , chemistry , physics , quantum mechanics , biology , computer vision , gene
Short-term forecasting of futures market is valuable and is also a technical challenge. In this paper, a hybrid approach for soybean oil futures price forecasting is proposed based on time-series analysis methods. The method combines wavelet transformation and exponential smoothing so that the characteristics of the time series can be captured at different time scales, and forecasting based on exponential smoothing is applied at each time scale. A comparative case study is then conducted that compares the proposed method with other three methods which are an RNN network with Long Short-Term Memory units, a Support-Vector Regression model, and an Exponential Smoothing model without wavelet decomposition to the time series. It could be concluded that the forecasting error performance of ES and Wavelet-ES was better than LSTM and SVR, and the Wavelet-ES achieved the best results for the direction forecasting. The case study provides valuable reference for application of short-term futures price forecasting.