Oil Price Forecasting Using Supervised GANs with Continuous Wavelet Transform Features

Zhaojie Luo, Jinhui Chen, Xiao Jing Cai, Katsuyuki Tanaka, Tetsuya Takiguchi, Takuji Kinkyo, Shigeyuki Hamori

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a novel approach based on a supervised Generative Adversarial Networks (GANs) model that forecasts the crude oil prices with Adaptive Scales Continuous Wavelet Transform (AS-CWT). In our study, we first confirmed that the possibility of using Continuous Wavelet Transform (CWT) to decompose an oil price series into various components, such as the sequence of days, weeks, months and years, so that the decomposed new time series can be used as inputs for a deep-learning (DL) training model. Second, we find that applying the proposed adaptive scales in the CWT method can strengthen the dependence of inputs and provide more useful information, which can improve the forecasting performance. Finally, we use the supervised GANs model as a training model, which can provide more accurate forecasts than those of the naive forecast (NF) model and other nonlinear models, such as Neural Networks (NNs), and Deep Belief Networks (DBNs) when dealing with a limited amount of oil prices data.

Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages830-835
Number of pages6
ISBN (Electronic)9781538637883
DOIs
Publication statusPublished - Nov 26 2018
Externally publishedYes
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: Aug 20 2018Aug 24 2018

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2018-August
ISSN (Print)1051-4651

Conference

Conference24th International Conference on Pattern Recognition, ICPR 2018
CountryChina
CityBeijing
Period8/20/188/24/18

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ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

Cite this

Luo, Z., Chen, J., Cai, X. J., Tanaka, K., Takiguchi, T., Kinkyo, T., & Hamori, S. (2018). Oil Price Forecasting Using Supervised GANs with Continuous Wavelet Transform Features. In 2018 24th International Conference on Pattern Recognition, ICPR 2018 (pp. 830-835). [8546240] (Proceedings - International Conference on Pattern Recognition; Vol. 2018-August). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2018.8546240