arXiv:2503.24007v4 Announce Type: replace Abstract: In time series forecasting, covariates represent external factors that influence target variables. Some covariates are observable only in the past (observed covariates, such as recorded weather data), while others are known in advance (known covariates, such as calendar events or discount schedules). Although covariates have the potential to enhance forecasting performance, most deep learning-based forecasting models struggle to address the length discrepancy between variables caused by the future portion of known covariates and fail to lever
The paper addresses a current limitation in deep learning for time series forecasting, specifically how to effectively integrate both historical and future-known covariate data to improve predictability.
Improved time series forecasting with complex covariate integration has wide applications across various industries, enhancing operational efficiency and decision-making for those relying on predictive models.
The proposed CITRAS model provides a robust method for handling the length discrepancy in time series covariates, potentially leading to more accurate and reliable predictions in fields like finance, supply chain, and energy.
- · Logistics and Supply Chain
- · Energy Trading Firms
- · Financial Institutions
- · AI/ML Research Community
- · Traditional forecasting models lacking covariate integration
- · Organizations relying on less sophisticated predictive analytics
More accurate forecasts lead to better resource allocation and reduced waste in industries dependent on time series predictions.
Enhanced predictive capabilities could accelerate automation of decision-making processes, particularly in operational planning.
The widespread adoption of such models might increase demand for specialized data scientists and sophisticated computing infrastructure to handle complex data integration.
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