TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting
<|reference_start|>TwinS: Revisiting Non-Stationarity in Multivariate Time Series Forecasting: Recently, multivariate time series forecasting tasks have garnered increasing attention due to their significant practical applications, leading to the emergence of various deep forecasting models. However, real-world time series exhibit pronounced non-stationary distribution characteristics. These characteristics are not solely limited to time-varying statistical properties highlighted by non-stationary Transformer but also encompass three key aspects: nested periodicity, absence of periodic distributions, and hysteresis among time variables. In this paper, we begin by validating this theory through wavelet analysis and propose the Transformer-based TwinS model, which consists of three modules to address the non-stationary periodic distributions: Wavelet Convolution, Period-Aware Attention, and Channel-Temporal Mixed MLP. Specifically, The Wavelet Convolution models nested periods by scaling the convolution kernel size like wavelet transform. The Period-Aware Attention guides attention computation by generating period relevance scores through a convolutional sub-network. The Channel-Temporal Mixed MLP captures the overall relationships between time series through channel-time mixing learning. TwinS achieves SOTA performance compared to mainstream TS models, with a maximum improvement in MSE of 25.8\% over PatchTST.<|reference_end|>
@article{hu2024twins:,
    title={TwinS: Revisiting Non-Stationarity in Multivariate Time Series
  Forecasting},
    author={Jiaxi Hu, Qingsong Wen, Sijie Ruan, Li Liu, Yuxuan Liang},
    journal={arXiv preprint arXiv:2406.03710},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.03710},
    primaryClass={cs.LG cs.AI}
}