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Goal-Oriented Time-Series Forecasting: Foundation Framework Design
Created by
Haebom
Author
Luca-Andrei Fechete, Mohamed Sana, Fadhel Ayed, Nicola Piovesan, Wenjie Li, Antonio De Domenico, Tareq Si Salem
Outline
This paper highlights that existing time series forecasting methods focus on minimizing overall prediction error without considering the differences in the importance of prediction horizons in downstream applications. Therefore, we propose a training methodology that allows prediction models to focus on application-specific regions of interest during inference without retraining. This method segments the prediction space into fine-grained segments during training and dynamically reweights and aggregates them to emphasize target horizons specified by the application. Unlike existing methods, this method allows for flexible, on-demand adjustments without pre-defining the horizons. Experiments on standard benchmarks and a newly collected wireless communication dataset demonstrate that the proposed method not only improves prediction accuracy within the region of interest but also provides measurable benefits in downstream task performance. These results highlight the potential for closer integration between predictive modeling and decision-making in real-world systems.
Takeaways, Limitations
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Takeaways:
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We present a novel methodology for tailoring time series forecasting models to application-specific domains of interest without retraining.
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Improved prediction accuracy within the region of interest and improved downstream task performance.
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Suggesting the potential for enhanced integration between predictive modeling and decision making.
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Limitations:
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Further research is needed on the generalization performance of the proposed method.
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Applicability verification is needed for various types of time series data.
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Research is needed to determine optimal segmentation and weight adjustment strategies for specific applications.
