WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series


  • Fuhao Yang Beijing Institute of Technology
  • Xin Li Beijing Institute of Technology
  • Min Wang Beijing Institute of Technology
  • Hongyu Zang Beijing Institute of Technology
  • Wei Pang Heriot-Watt University
  • Mingzhong Wang The University of the Sunshine Coast




ML: Time-Series/Data Streams, ML: Deep Neural Architectures, ML: Graph-based Machine Learning, ML: Representation Learning


Multivariate time series (MTS) analysis and forecasting are crucial in many real-world applications, such as smart traffic management and weather forecasting. However, most existing work either focuses on short sequence forecasting or makes predictions predominantly with time domain features, which is not effective at removing noises with irregular frequencies in MTS. Therefore, we propose WaveForM, an end-to-end graph enhanced Wavelet learning framework for long sequence FORecasting of MTS. WaveForM first utilizes Discrete Wavelet Transform (DWT) to represent MTS in the wavelet domain, which captures both frequency and time domain features with a sound theoretical basis. To enable the effective learning in the wavelet domain, we further propose a graph constructor, which learns a global graph to represent the relationships between MTS variables, and graph-enhanced prediction modules, which utilize dilated convolution and graph convolution to capture the correlations between time series and predict the wavelet coefficients at different levels. Extensive experiments on five real-world forecasting datasets show that our model can achieve considerable performance improvement over different prediction lengths against the most competitive baseline of each dataset.




How to Cite

Yang, F., Li, X., Wang, M., Zang, H., Pang, W., & Wang, M. (2023). WaveForM: Graph Enhanced Wavelet Learning for Long Sequence Forecasting of Multivariate Time Series. Proceedings of the AAAI Conference on Artificial Intelligence, 37(9), 10754-10761. https://doi.org/10.1609/aaai.v37i9.26276



AAAI Technical Track on Machine Learning IV