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arXiv Machine Learning

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.

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arXiv Machine Learning
Aug 27

NVExplain: Explaining Time Series Forecasting with Latent Trajectory Analysis and Structure-Preserving Surrogates

NVExplain is a model‑agnostic framework that explains time‑series forecasting by attributing each forecast horizon to temporally relevant historical lags. It models forecasting as a latent trajectory, introduces semantic flow to track information evolution, and aggregates this into a lag‑horizon attribution matrix. The method also generates structure‑preserving perturbations and fits sparse local surrogates to produce human‑readable, temporally coherent explanations, and demonstrates competitive faithfulness and stability across benchmark datasets.

By Muyan Anna Li, Manikandan Ravikiran, Aditi Gautam
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arXiv AI
Sep 25

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

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.

By Xin-Yu Hu, Shuang Liang, Cheng Feng, Shao-Qun Zhang
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arXiv AI
Aug 17

Forecast Collapse in Time-Series Foundation Models

arXiv:2608. 14106v1 Announce Type: cross Abstract: When forecasting hourly returns for 1,000 US equities, we observe an unexpected phenomenon: predictions become nearly flat and show poor stock ranking, as measured by cross-sectional correlation.

By Shu Wan, Miles Ma, Hank Zhu, Guangqi Liu, Stephen Wang, Qingsong Wen, Huan Liu
benchmarks
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Hugging Face Trending Papers
Jun 25

How Good Can Linear Models Be for Time-Series Forecasting?

Time-series forecasting research has been moving steadily toward larger architectures, from specialized transformers to general-purpose foundation models, on the assumption that capacity is what unlocks accuracy. We take the opposite position: most of the gap can be closed at far lower cost by tuning preprocessing rather than scaling models.

llmsbenchmarks
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