DWT Denoising for Multi-variate Time Series Forecasting

Zhifeng Wan, Peng Gong, Xin Li, Zhen Zhang, Fuhao Yang, Wei Pang

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

1 Citation (Scopus)
226 Downloads (Pure)


Multivariate time series data is ubiquitous in the real world, and the study of its modeling and analysis is a popular research topic in meteorology, transportation, finance and other fields. In these studies, classical statistical methods are primarily aimed at single time series analysis, while deep learning demonstrates the power to mine patterns from massive amounts of data. A major application of these studies is to analyze collected historical sequence information to predict what will happen over time in the future. Currently, recurrent neural network-based models and time-convolution-based models realize the predictive power of multivariate time series, but these deep models perform mediocrely at predicting long-sequence tasks. On the one hand, due to the accumulation of errors, on the other hand, the fact that the collected sequence contains a large amount of high-frequency noise. In order to improve the prediction accuracy of the model and mine more valuable features from the series, we propose a novel multivariate time series prediction framework ADWT for time series modeling. By designing an adaptive filtering module in the characteristics of the signal frequency domain, our model removes noise from some of the time series and builds an end-to-end framework by fusing it with the prediction module of deep learning. Experimental results show that our model can effectively improve the prediction accuracy of multivariate time series, and its performance in the three benchmark data sets is competitive with the latest spatial-temporal series prediction model, and has good interpretability.

Original languageEnglish
Title of host publicationFourteenth International Conference on Digital Image Processing (ICDIP 2022)
EditorsXudong Jiang, Wenbing Tao, Deze Zeng, Yi Xie
ISBN (Electronic)9781510657571
ISBN (Print)9781510657564
Publication statusPublished - 12 Oct 2022
Event14th International Conference on Digital Image Processing 2022 - Wuhan, China
Duration: 20 May 202223 May 2022

Publication series

NameProceedings of SPIE
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


Conference14th International Conference on Digital Image Processing 2022
Abbreviated titleICDIP 2022


  • Data Mining
  • Deep Learning
  • Discrete Wavelet Transform
  • Multi-variate time series forecasting

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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