PPDformer: Channel-Specific Periodic Patch Division for Time Series Forecasting

Multivariate time series (MTS) forecasting presents significant challenges due to the diverse noise distributions and complex periodic patterns across different channels. Existing Transformer-based models often apply uniform noise reduction techniques and simplistic patch segmentation, resulting in suboptimal performance in capturing fine-grained periodic dependencies. In this paper, we propose PPDformer, which independently denoises each channel’s data and identifies key periodic components using Short Time Fourier Transform (STFT). Additionally, we present a novel period-based patch segmentation strategy with period clustering, which transforms 1D time series data into 2D patches based on the identified periodicity. Furthermore, we design a dual attention mechanism for local and global information aggregation. Extensive experiments on public datasets demonstrate that PPDformer achieves state-of-the-art forecasting accuracy, particularly in scenarios with complex periodicity and noise. Code is available at https://github.com/damonwan1/PPDformer.