WiSPformer: A Wiener-informed Signal-Preserving Spectral Transformer for Multivariate Time Series Forecasting

Frequency-domain time-series forecasting has emerged as an effective alternative to time-domain modelling for multivariate time-series forecasting, because spectral representations compactly capture global trends and periodic patterns. However, the difficulty of frequency-based forecasting is the global spectral shrinkage which suppresses not only nuisance noise but also predictive local transients. In this paper, we propose WiSPformer, a Wiener-informed Signal-Preserving Transformer that combines a learnable Wiener Fourier filter, an adaptive signal-preserving mechanism, and a dual-channel complex spectral Transformer. Our learnable Wiener filter infers a frequency-wise spectral gain from the estimated signal and noise spectra. To overcome over-smoothing while retaining the benefits of spectral denoising, our signal-preserving method imposes a learnable protection on attenuation for channels with informative local dynamics. Lastly, we incorporate a dual-branch complex spectral Transformer to leverage synergistic dependencies between channels and frequency components. Extensive experiments on nine benchmark datasets show that WiSPformer consistently outperforms Wiener-only spectral filtering and achieves competitive performance compared with strong state-of-the-art baselines.