XLinear: Frequency-Enhanced MLP with CrossFilter for Robust Long-Range Forecasting
arXiv:2603.15645v1 Announce Type: new Abstract: Time series forecasters are widely used across various domains. Among them, MLP (multi-layer perceptron)-based forecasters have been proven to be more robust to noise compared to Transformer-based forecasters. However, MLP struggles to capture complex features, resulting in limitations on capturing long-range dependencies. To address this challenge, we propose XLinear, an MLP-based forecaster for long-range forecasting. Firstly, we decompose the time series into trend and seasonal components. For the trend component which contains long-range characteristics, we design Enhanced Frequency Attention (EFA) to capture long-term dependencies by leveraging frequency-domain operations. Additionally, a CrossFilter Block is proposed for the seasonal component to maintain the model's robustness to noise, avoiding the problems of low robustness often caused by attention mechanisms. Experimental results demonstrate that XLinear achieves state-of-the-
arXiv:2603.15645v1 Announce Type: new Abstract: Time series forecasters are widely used across various domains. Among them, MLP (multi-layer perceptron)-based forecasters have been proven to be more robust to noise compared to Transformer-based forecasters. However, MLP struggles to capture complex features, resulting in limitations on capturing long-range dependencies. To address this challenge, we propose XLinear, an MLP-based forecaster for long-range forecasting. Firstly, we decompose the time series into trend and seasonal components. For the trend component which contains long-range characteristics, we design Enhanced Frequency Attention (EFA) to capture long-term dependencies by leveraging frequency-domain operations. Additionally, a CrossFilter Block is proposed for the seasonal component to maintain the model's robustness to noise, avoiding the problems of low robustness often caused by attention mechanisms. Experimental results demonstrate that XLinear achieves state-of-the-art performance on test datasets. While keeping the lightweight architecture and high robustness of MLP-based models, our forecaster outperforms other MLP-based forecasters in capturing long-range dependencies.
Executive Summary
This article introduces XLinear, an MLP-based forecaster designed for long-range forecasting. The proposed methodology involves decomposing time series into trend and seasonal components, utilizing Enhanced Frequency Attention (EFA) for the trend component to capture long-term dependencies, and a CrossFilter Block for the seasonal component to maintain robustness to noise. Experimental results demonstrate XLinear's state-of-the-art performance on test datasets. The forecaster's capability to capture long-range dependencies while retaining the lightweight architecture and robustness of MLP-based models makes it a compelling solution. Although the article presents promising results, further analysis is needed to fully comprehend the algorithm's potential applications and limitations.
Key Points
- ▸ XLinear decomposes time series into trend and seasonal components for long-range forecasting.
- ▸ Enhanced Frequency Attention (EFA) captures long-term dependencies in the trend component.
- ▸ CrossFilter Block maintains robustness to noise in the seasonal component.
Merits
Strength
XLinear demonstrates state-of-the-art performance on test datasets, outperforming other MLP-based forecasters in capturing long-range dependencies.
Demerits
Limitation
The article lacks a thorough comparison with Transformer-based forecasters, which have been proven to be more robust to noise in certain scenarios.
Expert Commentary
The article presents a promising approach to long-range forecasting using XLinear, an MLP-based forecaster. The proposed methodology demonstrates a clear understanding of the challenges associated with capturing long-range dependencies and offers innovative solutions to address these challenges. However, a more comprehensive comparison with existing methods, including Transformer-based forecasters, would strengthen the article's conclusions. Additionally, further analysis of XLinear's performance in various domains and scenarios is necessary to fully evaluate its potential applications and limitations. Nevertheless, the article provides a valuable contribution to the field of time series forecasting and highlights the potential of MLP-based models for long-range forecasting.
Recommendations
- ✓ Future research should focus on a more thorough comparison of XLinear with existing methods, including Transformer-based forecasters.
- ✓ A more detailed analysis of XLinear's performance in various domains and scenarios is necessary to fully evaluate its potential applications and limitations.