Time series forecasting based on the wavelet transform

Tiantian Guo

doi:10.1117/12.2684645

21 July 2023 Time series forecasting based on the wavelet transform

Tiantian Guo

Proceedings Volume 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023); 127170K (2023) https://doi.org/10.1117/12.2684645
Event: 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 2023, Wuhan, China

Abstract

Long Sequence Time-series Forecasting (LSTF) plays a crucial role in data-driven decision-making tasks in many fields. Transformer-based prediction methods significantly improve the LSTF challenges, but existing studies ignore the effect of noise in the series. Therefore, this paper proposes the time series forecasting model based on the wavelet transform, and it has the following main features: (1) This paper decomposes the time series with the wavelet transform and models the time series data from time and frequency domains, preserving the low-frequency components to keep the compactness of the time series; (2) This model effectively filters out high-frequency interference by combining the Fourier transform with a filter, while preserving the integrity of the effective signal; (3) Internal dependence of noise reduction sequences through the self-attentive mechanism to establish the connection between high and low-frequency signals. In this paper, experiments are conducted on four real data sets, and the performance of this model is significantly improved compared with existing advanced prediction models.

Citation Download Citation

Tiantian Guo "Time series forecasting based on the wavelet transform", Proc. SPIE 12717, 3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023), 127170K (21 July 2023); https://doi.org/10.1117/12.2684645

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available