Goal-Oriented Time-Series Forecasting: Foundation Framework Design

2025-04-24

AI Summary

Traditional time-series forecasting typically minimizes global prediction error, overlooking downstream tasks’ heterogeneous requirements across different forecast horizons. This paper introduces a task-oriented forecasting paradigm that employs a task-driven, dynamically segmented weighting training mechanism, enabling models to adaptively prioritize learning for time segments according to their importance to downstream applications (e.g., resource scheduling). The method comprises three key components: (i) segmented forecast decomposition, (ii) importance-aware dynamic weight fusion, and (iii) an end-to-end loss function explicitly aligned with decision-making objectives. Evaluated on multiple standard benchmarks and a custom wireless communication dataset, the approach achieves significant improvements in both forecasting accuracy and downstream task performance. Notably, it is the first to realize joint optimization of prediction fidelity and decision-level objectives.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Traditional time-series forecasting often focuses only on minimizing prediction errors, ignoring the specific requirements of real-world applications that employ them. This paper presents a new training methodology, which allows a forecasting model to dynamically adjust its focus based on the importance of forecast ranges specified by the end application. Unlike previous methods that fix these ranges beforehand, our training approach breaks down predictions over the entire signal range into smaller segments, which are then dynamically weighted and combined to produce accurate forecasts. We tested our method on standard datasets, including a new dataset from wireless communication, and found that not only it improves prediction accuracy but also improves the performance of end application employing the forecasting model. This research provides a basis for creating forecasting systems that better connect prediction and decision-making in various practical applications.

Problem

• Dynamic adjustment of forecasting focus based on application needs
• Improving prediction accuracy and end-application performance
• Bridging forecasting and decision-making in practical applications

Innovation

• Dynamic focus adjustment based on application needs
• Segmented prediction with dynamic weighting
• Improved accuracy and application performance