Temporal Window Smoothing of Exogenous Variables for Improved Time Series Prediction
This work addresses challenges in time series forecasting for applications relying on external data, though it is incremental as it refines existing methods rather than introducing a new paradigm.
The paper tackled the problem of redundancy and limited long-term dependency capture in transformer-based time series forecasting models using exogenous inputs, by proposing a method that whitens exogenous inputs based on global statistics to improve awareness of patterns over extended periods, achieving state-of-the-art performance on four benchmark datasets and outperforming 11 baseline models.
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.