Time series forecasting based on the wavelet transform
Tiantian Guo · 2023
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.