Temporal Window Smoothing of Exogenous Variables for Improved Time Series Prediction

TL;DR

This paper introduces a novel method for preprocessing exogenous variables in time series forecasting, reducing redundancy and capturing long-term patterns, leading to improved accuracy without increasing look-back windows.

Contribution

We propose a global statistics-based whitening technique for exogenous inputs, enhancing long-term dependency capture and model performance in transformer-based time series forecasting.

Findings

Achieved state-of-the-art results on four benchmark datasets.

Consistently outperformed 11 baseline models.

Improved forecasting accuracy without longer look-back windows.

Abstract

Although most transformer-based time series forecasting models primarily depend on endogenous inputs, recent state-of-the-art approaches have significantly improved performance by incorporating external information through exogenous inputs. However, these methods face challenges, such as redundancy when endogenous and exogenous inputs originate from the same source and limited ability to capture long-term dependencies due to fixed look-back windows. In this paper, we propose a method that whitens the exogenous input to reduce redundancy that may persist within the data based on global statistics. Additionally, our approach helps the exogenous input to be more aware of patterns and trends over extended periods. By introducing this refined, globally context-aware exogenous input to the endogenous input without increasing the lookback window length, our approach guides the model towards improved forecasting. Our approach achieves state-of-the-art performance in four benchmark datasets, consistently outperforming 11 baseline models. These results establish our method as a robust and effective alternative for using exogenous inputs in time series forecasting.