WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting
This addresses the need for more accurate and efficient forecasting in applications like weather, power load, and finance, representing an incremental improvement over existing MLP-mixer models.
The paper tackles the problem of improving long-term time series forecasting by proposing WPMixer, a novel MLP-based model that uses multi-resolution wavelet decomposition, patching, and MLP mixing, and it significantly outperforms state-of-the-art models in a computationally efficient way.
Time series forecasting is crucial for various applications, such as weather forecasting, power load forecasting, and financial analysis. In recent studies, MLP-mixer models for time series forecasting have been shown as a promising alternative to transformer-based models. However, the performance of these models is still yet to reach its potential. In this paper, we propose Wavelet Patch Mixer (WPMixer), a novel MLP-based model, for long-term time series forecasting, which leverages the benefits of patching, multi-resolution wavelet decomposition, and mixing. Our model is based on three key components: (i) multi-resolution wavelet decomposition, (ii) patching and embedding, and (iii) MLP mixing. Multi-resolution wavelet decomposition efficiently extracts information in both the frequency and time domains. Patching allows the model to capture an extended history with a look-back window and enhances capturing local information while MLP mixing incorporates global information. Our model significantly outperforms state-of-the-art MLP-based and transformer-based models for long-term time series forecasting in a computationally efficient way, demonstrating its efficacy and potential for practical applications.