XLinear: A Lightweight and Accurate MLP-Based Model for Long-Term Time Series Forecasting with Exogenous Inputs
Xinyang Chen, Huidong Jin, Yu Huang, Zaiwen Feng

TL;DR
XLinear is a lightweight MLP-based model designed for long-term time series forecasting that effectively leverages exogenous inputs and outperforms existing models in accuracy and efficiency.
Contribution
The paper introduces XLinear, a novel MLP-based architecture that efficiently captures temporal and exogenous variable dependencies for improved long-term forecasting.
Findings
XLinear achieves superior accuracy on seven benchmarks.
XLinear demonstrates higher efficiency compared to transformer-based models.
XLinear effectively models the influence of exogenous variables.
Abstract
Despite the prevalent assumption of uniform variable importance in long-term time series forecasting models, real world applications often exhibit asymmetric causal relationships and varying data acquisition costs. Specifically, cost-effective exogenous data (e.g., local weather) can unilaterally influence dynamics of endogenous variables, such as lake surface temperature. Exploiting these links enables more effective forecasts when exogenous inputs are readily available. Transformer-based models capture long-range dependencies but incur high computation and suffer from permutation invariance. Patch-based variants improve efficiency yet can miss local temporal patterns. To efficiently exploit informative signals across both the temporal dimension and relevant exogenous variables, this study proposes XLinear, a lightweight time series forecasting model built upon MultiLayer Perceptrons…
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Taxonomy
TopicsHydrological Forecasting Using AI · Traffic Prediction and Management Techniques · Forecasting Techniques and Applications
