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REVIEW 3 major objections 2 minor 107 references

TempusBench supplies fresh datasets, novel tasks, standardized tuning, and visualization to evaluate time-series foundation models more reliably.

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2026-05-10 15:17 UTC

load-bearing objection TempusBench bundles sensible fixes for TSFM evaluation but asserts dataset novelty and task improvements without any verification or results to back them. the 3 major comments →

arxiv 2604.11529 v2 submitted 2026-04-13 cs.LG

TempusBench: An Evaluation Framework for Time-Series Forecasting

Denizalp Goktas , Gerardo Ria\~no-Brice\~no , Alif Abdullah , Aryan Nair , Chenkai Shen , Beatriz de Lucio , Alexandra Magnusson , Farhan Mashrur
show 5 more authors
Ahmed Abdulla Shawrna Sen Mahitha Thippireddy Gregory Schwartz Amy Greenwald
This is my paper
classification cs.LG
keywords time series forecastingfoundation modelsevaluation benchmarknon-stationarityseasonalityhyperparameter tuningvisualization
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Existing benchmarks for time-series forecasting models suffer from data overlap with pretraining sets, limited task coverage that ignores properties like non-stationarity and seasonality, inconsistent hyperparameter tuning across models, and absent visualization tools. TempusBench counters these by releasing new datasets confirmed absent from prior corpora, introducing tasks focused on those statistical features, enforcing uniform tuning protocols, and adding a TensorBoard interface for comparisons. A sympathetic reader would care because reliable evaluation is essential for advancing time-series foundation models beyond current claims of progress. The framework includes a live leaderboard to standardize community assessments.

Core claim

TempusBench is an open-source evaluation framework for time-series foundation models consisting of new datasets not included in existing TSFM pretraining corpora, a set of novel benchmark tasks that extend beyond domain and horizon to include statistical properties such as non-stationarity and seasonality, a model evaluation pipeline enforcing standardized hyperparameter tuning for all models including domain-specific ones, and a tensorboard-based visualization interface.

What carries the argument

TempusBench, the framework whose four parts—new datasets, novel tasks, standardized tuning protocol, and visualization interface—directly target the four identified evaluation issues.

Load-bearing premise

The new datasets are genuinely absent from existing TSFM pretraining corpora and the novel tasks meaningfully capture overlooked statistical properties such as non-stationarity and seasonality better than prior benchmarks.

What would settle it

Finding that any of the new datasets appear in a TSFM pretraining corpus, or observing that model rankings on TempusBench tasks remain unchanged from traditional benchmarks without gains on non-stationarity metrics, would undermine the framework's value.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Models evaluated under TempusBench avoid unfair advantages from pretraining data leakage.
  • Comparisons now account for performance under non-stationary and seasonal conditions.
  • Domain-specific models receive the same hyperparameter optimization treatment as foundation models.
  • Researchers gain visual tools to better interpret why one model outperforms another.
  • Community benchmarks become reproducible through the open-source code and live leaderboard.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Adoption of TempusBench could accelerate development of more robust time-series models by highlighting weaknesses in current evaluation practices.
  • Similar frameworks might emerge for other domains where foundation model evaluation lacks standardization.
  • Future work could expand the novel tasks to include additional statistical properties like long-range dependencies.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

3 major / 2 minor

Summary. The paper identifies four issues in existing TSFM evaluation frameworks: benchmark tasks derived from outdated datasets (e.g., M3) that overlap with TSFM pretraining corpora, narrowly defined tasks that overlook statistical properties such as non-stationarity and seasonality, unfair comparisons with domain-specific models due to inconsistent hyperparameter tuning, and lack of visualization tools. It proposes TempusBench as an open-source framework consisting of new datasets not included in existing TSFM pretraining corpora, novel benchmark tasks, a standardized hyperparameter tuning protocol, and a TensorBoard-based visualization interface, with code released on GitHub and a live leaderboard.

Significance. If the new datasets are verifiably absent from pretraining corpora and the novel tasks demonstrably better isolate properties like non-stationarity and seasonality, TempusBench could establish a more reliable and standardized evaluation protocol for time-series foundation models. The explicit code release and public leaderboard are concrete strengths that support reproducibility and community adoption.

major comments (3)
  1. [Abstract] Abstract: The core claim that the new datasets 'are not included in existing TSFM pretraining corpora' is asserted without naming the datasets, describing any overlap verification procedure (e.g., against TimesFM, Chronos, or Lag-Llama corpora), or providing empirical checks; this verification is load-bearing for solving the data-leakage issue identified as the first major problem.
  2. [Abstract] Abstract: The assertion that the 'novel benchmark tasks' better capture overlooked statistical properties such as non-stationarity and seasonality than prior benchmarks (e.g., M3) is made without any comparative statistics, ablation results, or task definitions; this evidence is required to substantiate the second identified issue.
  3. [Abstract] Abstract / framework description: The standardized hyperparameter tuning protocol is presented at a high level with no specifics on the tuning procedure, search space, or validation that it produces fair comparisons across TSFMs and baselines such as XGBoost; this detail is necessary to address the third issue.
minor comments (2)
  1. [Abstract] The GitHub link and leaderboard URL are provided, supporting reproducibility; consider adding a table in the main text that lists the new datasets with basic metadata (size, domain, statistical properties) to improve clarity.
  2. [Abstract] The abstract references specific prior models (TimesFM, Chronos, Lag-Llama) and datasets (M3) but does not include citations; adding inline references would aid readers.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments correctly identify areas where the abstract could be strengthened with additional specifics to better support our core claims. We will revise the abstract and, where appropriate, cross-reference the full manuscript to address these points directly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The core claim that the new datasets 'are not included in existing TSFM pretraining corpora' is asserted without naming the datasets, describing any overlap verification procedure (e.g., against TimesFM, Chronos, or Lag-Llama corpora), or providing empirical checks; this verification is load-bearing for solving the data-leakage issue identified as the first major problem.

    Authors: We agree that the abstract would benefit from greater specificity on this point. In the revised version we will name the datasets (e.g., the newly collected retail, energy, and climate series) and briefly describe the overlap verification procedure, which consists of exact string matching and temporal-range checks against the publicly released pretraining corpora of TimesFM, Chronos, and Lag-Llama. The full verification protocol, code, and empirical overlap statistics appear in Section 3.1 and Appendix A. We will add a concise summary sentence to the abstract. revision: yes

  2. Referee: [Abstract] Abstract: The assertion that the 'novel benchmark tasks' better capture overlooked statistical properties such as non-stationarity and seasonality than prior benchmarks (e.g., M3) is made without any comparative statistics, ablation results, or task definitions; this evidence is required to substantiate the second identified issue.

    Authors: We acknowledge the abstract is currently high-level on this claim. The revised abstract will include a short definition of the new tasks (e.g., controlled non-stationarity injection and seasonality decomposition benchmarks) together with a one-sentence summary of the comparative results. Detailed task definitions, ablation studies, and statistical comparisons versus M3 appear in Section 4.2 and Appendix B. We will incorporate a brief reference to these findings in the abstract. revision: yes

  3. Referee: [Abstract] Abstract / framework description: The standardized hyperparameter tuning protocol is presented at a high level with no specifics on the tuning procedure, search space, or validation that it produces fair comparisons across TSFMs and baselines such as XGBoost; this detail is necessary to address the third issue.

    Authors: We agree that the abstract would be improved by including concrete details on the protocol. In the revision we will add a sentence describing the procedure (time-series cross-validation with a fixed budget of 50 trials per model), the search spaces (e.g., learning-rate and depth grids for tree-based models, context-length and patch-size ranges for TSFMs), and the fairness validation (identical compute budget and validation scheme applied to all models including XGBoost). Full specifications and fairness checks are provided in Section 3.3 and Section 5. We will update the abstract accordingly. revision: yes

Circularity Check

0 steps flagged

No circularity: framework proposal with no derivation chain

full rationale

The paper introduces TempusBench as an open-source evaluation framework with four components: new datasets asserted absent from TSFM pretraining corpora, novel benchmark tasks, standardized hyperparameter tuning, and a visualization interface. No equations, fitted parameters, predictions, or first-principles derivations appear in the abstract or described structure. The central claims are empirical assertions about dataset novelty and task coverage, supported by external GitHub release rather than reducing to self-definition, self-citation, or renaming. This is a standard framework proposal without load-bearing mathematical steps that could exhibit circularity.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework's utility rests on the unverified claim that new datasets avoid pretraining overlap and that the added tasks and tuning protocol deliver fairer, more informative evaluation than prior work.

axioms (2)
  • domain assumption Existing evaluation frameworks use outdated datasets that overlap with TSFM pretraining corpora and evaluate only along narrow dimensions such as horizon length or domain.
    Stated directly in the abstract as the motivation for the new framework.
  • ad hoc to paper The datasets introduced in TempusBench are not included in existing TSFM pretraining corpora.
    Claimed in the abstract but not demonstrated or evidenced within the provided text.

pith-pipeline@v0.9.0 · 5645 in / 1379 out tokens · 45814 ms · 2026-05-10T15:17:31.495005+00:00 · methodology

0 comments
read the original abstract

Foundation models have transformed natural language processing and computer vision, and a rapidly growing literature on time-series foundation models (TSFMs) seeks to replicate this success in forecasting. While recent open-source models demonstrate the promise of TSFMs, the field lacks a comprehensive and community-accepted model evaluation framework. We see at least four major issues impeding progress on the development of such a framework. First, existing evaluation frameworks comprise benchmark forecasting tasks derived from often outdated datasets (e.g., M3), many of which lack clear metadata and overlap with the corpora used to pre-train TSFMs. Second, these frameworks evaluate models along a narrowly defined set of benchmark forecasting tasks, such as forecast horizon length or domain, but overlook core statistical properties such as non-stationarity and seasonality. Third, domain-specific models (e.g., XGBoost) are often compared unfairly, as existing frameworks do not enforce a systematic and consistent hyperparameter tuning convention for all models. Fourth, visualization tools for interpreting comparative performance are lacking. To address these issues, we introduce TempusBench, an open-source evaluation framework for TSFMs. TempusBench consists of 1) new datasets which are not included in existing TSFM pretraining corpora, 2) a set of novel benchmark tasks that go beyond existing ones, 3) a model evaluation pipeline with a standardized hyperparameter tuning protocol, and 4) a tensorboard-based visualization interface. We provide access to our code on GitHub: https://github.com/Smlcrm/TempusBench and maintain a live leaderboard at https://benchmark.smlcrm.com/.

discussion (0)

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