# Goal-Oriented Time-Series Forecasting: Foundation Framework Design

arXiv: https://arxiv.org/abs/2504.17493 · HTML: https://mlsanity.com/paper/2504.17493/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 49 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Builds on (most cited first, 22 of 22)

- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [A Time Series is Worth 64 Words: Long-term Forecasting with Transformers](https://mlsanity.com/paper/2211.14730/) (2022)
- [Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting](https://mlsanity.com/paper/2106.13008/) (2021)
- [Are Transformers Effective for Time Series Forecasting?](https://mlsanity.com/paper/2205.13504/) (2022)
- [FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting](https://mlsanity.com/paper/2201.12740/) (2022)
- [iTransformer: Inverted Transformers Are Effective for Time Series Forecasting](https://mlsanity.com/paper/2310.06625/) (2023)
- [End-to-End Training of Deep Visuomotor Policies](https://mlsanity.com/paper/1504.00702/) (2015)
- [A decoder-only foundation model for time-series forecasting](https://mlsanity.com/paper/2310.10688/) (2023)
- [Unified Training of Universal Time Series Forecasting Transformers](https://mlsanity.com/paper/2402.02592/) (2024)
- [MOMENT: A Family of Open Time-series Foundation Models](https://mlsanity.com/paper/2402.03885/) (2024)
- [Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting](https://mlsanity.com/paper/2205.14415/) (2022)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [From Predictive to Prescriptive Analytics](https://mlsanity.com/paper/1402.5481/) (2014)
- [CARD: Channel Aligned Robust Blend Transformer for Time Series Forecasting](https://mlsanity.com/paper/2305.12095/) (2023)
- [Toto: Time Series Optimized Transformer for Observability](https://mlsanity.com/paper/2407.07874/) (2024)
- [Learning Surrogate Losses](https://mlsanity.com/paper/1905.10108/) (2019)
- [Deep Reinforcement Learning with Spatio-temporal Traffic Forecasting for Data-Driven Base Station Sleep Control](https://mlsanity.com/paper/2101.08391/) (2021)
- [Deep Imbalanced Time-series Forecasting via Local Discrepancy Density](https://mlsanity.com/paper/2302.13563/) (2023)
- [Load-Based On/Off Scheduling for Energy-Efficient Delay-Tolerant 5G Networks](https://mlsanity.com/paper/1812.11654/) (2018)
- [Loss Shaping Constraints for Long-Term Time Series Forecasting](https://mlsanity.com/paper/2402.09373/) (2024)
- [Flusion: Integrating multiple data sources for accurate influenza predictions](https://mlsanity.com/paper/2407.19054/) (2024)
- [Kairos: Energy-Efficient Radio Unit Control for O-RAN via Advanced Sleep Modes](https://mlsanity.com/paper/2501.15853/) (2025)

## Built on by (most cited first, 2 of 2)

- [A Family of Open Time-Series Foundation Models for the Radio Access Network](https://mlsanity.com/paper/2604.04271/) (2026)
- [Goal-oriented probabilistic forecasting for dynamic PRB allocation in 5G networks](https://mlsanity.com/paper/2609.17297/) (2026)

## Abstract (arXiv)

Conventional time-series forecasting methods typically aim to minimize overall prediction error, without accounting for the varying importance of different forecast ranges in downstream applications. We propose a training methodology that enables forecasting models to adapt their focus to application-specific regions of interest at inference time, without retraining. The approach partitions the prediction space into fine-grained segments during training, which are dynamically reweighted and aggregated to emphasize the target range specified by the application. Unlike prior methods that predefine these ranges, our framework supports flexible, on-demand adjustments. Experiments on standard benchmarks and a newly collected wireless communication dataset demonstrate that our method not only improves forecast accuracy within regions of interest but also yields measurable gains in downstream task performance. These results highlight the potential for closer integration between predictive modeling and decision-making in real-world systems.