WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting

• URL: http://arxiv.org/abs/2412.17176v1
• Date: Sun, 22 Dec 2024 22:08:16 GMT
• Title: WPMixer: Efficient Multi-Resolution Mixing for Long-Term Time Series Forecasting
• Authors: Md Mahmuddun Nabi Murad, Mehmet Aktukmak, Yasin Yilmaz,
• Abstract summary: We propose Wavelet Patch Patch Mixer (WPMixer), a novel multi-resolution wavelet decomposition model for time series forecasting.<n>Our model significantly outperforms state-of-the-art-based and transformer-based models for long-term time series forecasting.
• Score: 20.29110166475336
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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