Variable-Dynamic Multivariate Time-Series Forecasting for IoT Systems

The past decade has witnessed the success of deep learning-based multivariate time-series forecasting in Internet of Things (IoT) systems. However, dynamic variable correlation remains a long-standing problem. The majority of existing multivariate forecasting methods either constantly forbid the interactions of all variables or, conversely, keep extracting the correlations of all variables, which is suboptimal for real-world time-series with time-varying variable correlations. In contrast, we introduce a novel variable-dynamic forecasting transformer named VDformer. By leveraging empirical mode decomposition (EMD), VDformer can sparsely identify the dominant periodic ingredients of each variable in an arbitrary multivariate sequence via Fourier spectral analysis of its intrinsic mode functions (IMFs) obtained by the EMD. Thus, a mask matrix, where only the variables with identical dominant periodic ingredients are allowed for interactions, can be generated and used in the cross-variable attention modules of VDformer to dynamically gauge and extract the variable correlations. Additionally, better decoder initialization can be obtained by reconstructing the input sequence with these dominant periodic ingredients and extending the reconstructed results to the prediction duration. Extensive experiments on 11 benchmarks, which cover five IoT-related domains, demonstrate the state-of-the-art forecasting performance of VDformer (10.02% MSE reduction relative to the current best method). Code and Appendix are released on https://github.com/OrigamiSL/VDformer.