← Back to all news
arXiv AI By Xin-Yu Hu, Shuang Liang, Cheng Feng, Shao-Qun Zhang

SGA: Uncertainty Quantification for Multi-Step Forecasting in Time Series Foundation Models

Read the original on arXiv AI →

The paper introduces SGA, a method for quantifying uncertainty in multi‑step forecasts from Time Series Foundation Models (TSFMs). SGA models all possible forecast branches as a directed acyclic graph, using the graph’s complexity—derived from topology and TSFM stochasticity—to bound and measure uncertainty. Experiments on 11 TSFMs across 27 datasets show that SGA outperforms existing uncertainty‑quantification methods, offers broader sampling coverage, and reveals that larger TSFMs tend to produce lower uncertainty estimates.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Machine Learning
Sep 14

Explaining Time Series Forecasting with Horizon-Resolved Attribution

The paper introduces Horizon-Resolved eXplanation (HRX), a framework that adds a horizon axis to time‑series forecasting explanations, allowing each forecast step to have its own importance map. HRX operates as a plug‑in for any differentiable forecaster, includes an evaluation protocol that tests the impact of removing top‑ranked inputs, and a rank criterion to decide when horizon resolution is beneficial. Experiments across multiple backbones and datasets demonstrate that incorporating the horizon axis improves explanation quality and that the step‑wise dependence is low‑dimensional, requiring only a few shared maps regardless of forecast length.

By Seunghan Lee, Jun Seo, Jaehoon Lee, Junhyeok Kang, Sangjun Han, Sungdong Yoo, Minjae Kim, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Soonyoung Lee, Wonbin Ahn
More like this →
arXiv AI
Aug 5

FinVerse: Financial Time-Series Benchmark

arXiv:2608. 03259v1 Announce Type: cross Abstract: As time-series foundation models have emerged, the need for benchmarks that can evaluate their forecasting ability in meaningful ways has become increasingly important.

By Jaehoon Lee, Jun Seo, Seunghan Lee, Tae Yoon Lim, Dongwan Kang, Hwanil Choi, Minjae Kim, Sungdong Yoo, Junhyeok Kang, Sangjun Han, Soonyoung Lee, Wonbin Ahn
benchmarks
More like this →