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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.05284 |
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| _version_ | 1866908439917101056 |
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| author | Kamal, Mustafa Hashem, Niyaz Bin Krambroeckers, Robin Mohammed, Nabeel Rahman, Shafin |
| author_facet | Kamal, Mustafa Hashem, Niyaz Bin Krambroeckers, Robin Mohammed, Nabeel Rahman, Shafin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_05284 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Temporal Window Smoothing of Exogenous Variables for Improved Time Series Prediction Kamal, Mustafa Hashem, Niyaz Bin Krambroeckers, Robin Mohammed, Nabeel Rahman, Shafin Machine Learning 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. |
| title | Temporal Window Smoothing of Exogenous Variables for Improved Time Series Prediction |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2507.05284 |