Contamination Map Methodology

Time series / Model

TimesFM 1.0

Organisation
Google Research
Family
TimesFM · version 1.0
Released
2024-05-03 (HF repo creation of google/timesfm-1.0-200m (https://huggingface.co/api/models/google/timesfm-1.0-200m createdAt 2024-05-03); paper arXiv v1 submitted 2023-10-14 (https://arxiv.org/abs/2310.10688v1))
Checkpoints
google/timesfm-1.0-200m, google/timesfm-1.0-200m-pytorch
Weights
Open · Apache-2.0
Links
PaperModel cardCode

Training data disclosure: Full list

Level determined for 26% of benchmark cells.

“Table 1: Composition of TimesFM pretraining dataset.”
arxiv.org · paper · Table 1 · retrieved 2026-10-15
“We address this problem by sourcing the bulk of data used to train our models from three major sources: Google trends, Wiki Pageview statistics and synthetic time-series.”
arxiv.org · paper · Section 5 (Pretraining Details) · retrieved 2026-10-15

Other declared training data

Declared exclusions

Benchmark cells

GIFT-Eval

Unknown Jena Weather (GIFT-Eval) · training data not disclosed in enough detail

Unknown BizITObs - Application · training data not disclosed in enough detail

Unknown BizITObs - Service · training data not disclosed in enough detail

Unknown BizITObs - L2C · training data not disclosed in enough detail

Unknown Bitbrains - Fast Storage · training data not disclosed in enough detail

Unknown Bitbrains - rnd · training data not disclosed in enough detail

Unknown Restaurant (Recruit Restaurant Visitor Forecasting) · training data not disclosed in enough detail

Unknown ETT1 (GIFT-Eval) · training data not disclosed in enough detail

Unknown ETT2 (GIFT-Eval) · training data not disclosed in enough detail

Unknown Loop Seattle (LibCity) · training data not disclosed in enough detail

Unknown SZ-Taxi (LibCity) · training data not disclosed in enough detail

Unknown M_DENSE (LibCity) · training data not disclosed in enough detail

Unknown Solar (LSTNet, 137 Alabama PV plants) · training data not disclosed in enough detail

Unknown Hierarchical Sales (Mancuso et al., 2020) · training data not disclosed in enough detail

Seen, split unclear M4 Yearly · the dataset was used; the source does not say which part

Evidence (2)
“We add all the granularities of the M4 dataset [MSA22], the hourly and 15 minute Electricity and the hourly Traffic datasets (see [ZZP+ 21]).”
arxiv.org · paper · Section 5 (Pretraining Details) · retrieved 2026-10-15
“M4 quarterly”
arxiv.org · paper · Table 1 · retrieved 2026-10-15

Seen, split unclear M4 Quarterly · the dataset was used; the source does not say which part

Evidence (2)
“We add all the granularities of the M4 dataset [MSA22], the hourly and 15 minute Electricity and the hourly Traffic datasets (see [ZZP+ 21]).”
arxiv.org · paper · Section 5 (Pretraining Details) · retrieved 2026-10-15
“M4 quarterly”
arxiv.org · paper · Table 1 · retrieved 2026-10-15

Seen, split unclear M4 Monthly · the dataset was used; the source does not say which part

Evidence (2)
“We add all the granularities of the M4 dataset [MSA22], the hourly and 15 minute Electricity and the hourly Traffic datasets (see [ZZP+ 21]).”
arxiv.org · paper · Section 5 (Pretraining Details) · retrieved 2026-10-15
“M4 quarterly”
arxiv.org · paper · Table 1 · retrieved 2026-10-15

Seen, split unclear M4 Weekly · the dataset was used; the source does not say which part

Evidence (2)
“We add all the granularities of the M4 dataset [MSA22], the hourly and 15 minute Electricity and the hourly Traffic datasets (see [ZZP+ 21]).”
arxiv.org · paper · Section 5 (Pretraining Details) · retrieved 2026-10-15
“M4 quarterly”
arxiv.org · paper · Table 1 · retrieved 2026-10-15

Seen, split unclear M4 Daily · the dataset was used; the source does not say which part

Evidence (2)
“We add all the granularities of the M4 dataset [MSA22], the hourly and 15 minute Electricity and the hourly Traffic datasets (see [ZZP+ 21]).”
arxiv.org · paper · Section 5 (Pretraining Details) · retrieved 2026-10-15
“M4 quarterly”
arxiv.org · paper · Table 1 · retrieved 2026-10-15

Seen, split unclear M4 Hourly · the dataset was used; the source does not say which part

Evidence (2)
“We add all the granularities of the M4 dataset [MSA22], the hourly and 15 minute Electricity and the hourly Traffic datasets (see [ZZP+ 21]).”
arxiv.org · paper · Section 5 (Pretraining Details) · retrieved 2026-10-15
“M4 quarterly”
arxiv.org · paper · Table 1 · retrieved 2026-10-15

Unknown Hospital · training data not disclosed in enough detail

Unknown COVID Deaths · training data not disclosed in enough detail

Unknown US Births · training data not disclosed in enough detail

Unknown Saugeen River Flow · training data not disclosed in enough detail

Unknown Temperature Rain · training data not disclosed in enough detail

Unknown KDD Cup 2018 · training data not disclosed in enough detail

Unknown Car Parts · training data not disclosed in enough detail

Seen, split unclear ElectricityLoadDiagrams20112014 (UCI) · the dataset was used; the source does not say which part

Evidence (1)
“We add all the granularities of the M4 dataset [MSA22], the hourly and 15 minute Electricity and the hourly Traffic datasets (see [ZZP+ 21]).”
arxiv.org · paper · Section 5 (Pretraining Details) · retrieved 2026-10-15

Long-horizon forecasting (ETT, Electricity, Traffic, Weather, Exchange, ILI)

Not seen ETTh1 · the authors state the dataset was excluded from training

Evidence (2)
“These datasets have been intentionally held out from our pretraining data.”
arxiv.org · paper · Section 6 (Empirical Results) · retrieved 2026-10-15
“A few of these datasets are used in pretraining, so we focus on the other datasets in this collection (ETTm1, ETTm2. ETTh1 and ETTh2)”
arxiv.org · paper · Section 6, Informer paragraph · retrieved 2026-10-15

Not seen ETTh2 · the authors state the dataset was excluded from training

Evidence (2)
“These datasets have been intentionally held out from our pretraining data.”
arxiv.org · paper · Section 6 (Empirical Results) · retrieved 2026-10-15
“A few of these datasets are used in pretraining, so we focus on the other datasets in this collection (ETTm1, ETTm2. ETTh1 and ETTh2)”
arxiv.org · paper · Section 6, Informer paragraph · retrieved 2026-10-15

Not seen ETTm1 · the authors state the dataset was excluded from training

Evidence (2)
“These datasets have been intentionally held out from our pretraining data.”
arxiv.org · paper · Section 6 (Empirical Results) · retrieved 2026-10-15
“A few of these datasets are used in pretraining, so we focus on the other datasets in this collection (ETTm1, ETTm2. ETTh1 and ETTh2)”
arxiv.org · paper · Section 6, Informer paragraph · retrieved 2026-10-15

Not seen ETTm2 · the authors state the dataset was excluded from training

Evidence (2)
“These datasets have been intentionally held out from our pretraining data.”
arxiv.org · paper · Section 6 (Empirical Results) · retrieved 2026-10-15
“A few of these datasets are used in pretraining, so we focus on the other datasets in this collection (ETTm1, ETTm2. ETTh1 and ETTh2)”
arxiv.org · paper · Section 6, Informer paragraph · retrieved 2026-10-15

Seen, split unclear Electricity (ECL, 321 clients) · the dataset was used; the source does not say which part

Evidence (1)
“We add all the granularities of the M4 dataset [MSA22], the hourly and 15 minute Electricity and the hourly Traffic datasets (see [ZZP+ 21]).”
arxiv.org · paper · Section 5 (Pretraining Details) · retrieved 2026-10-15

Seen, split unclear Traffic (PeMS San Francisco Bay, 862 sensors) · the dataset was used; the source does not say which part

Evidence (1)
“We add all the granularities of the M4 dataset [MSA22], the hourly and 15 minute Electricity and the hourly Traffic datasets (see [ZZP+ 21]).”
arxiv.org · paper · Section 5 (Pretraining Details) · retrieved 2026-10-15

Unknown Weather (Jena, 2020; Autoformer) · training data not disclosed in enough detail

Unknown Exchange-Rate · training data not disclosed in enough detail

Unknown ILI (CDC influenza-like illness, national) · training data not disclosed in enough detail

Time-series anomaly detection

Unknown TSB-AD-U (TSB-AD univariate) · training data not disclosed in enough detail

Unknown TSB-AD-M (TSB-AD multivariate) · training data not disclosed in enough detail

Unknown UCR Time Series Anomaly Archive (Hexagon ML/UCR, KDD Cup 2021) · training data not disclosed in enough detail

Unknown Server Machine Dataset (SMD) · training data not disclosed in enough detail

Unknown Mars Science Laboratory (MSL) telemetry anomaly dataset · training data not disclosed in enough detail

Unknown Soil Moisture Active Passive (SMAP) telemetry anomaly dataset · training data not disclosed in enough detail

Unknown Pooled Server Metrics (PSM) · training data not disclosed in enough detail

Unknown Secure Water Treatment (SWaT) · training data not disclosed in enough detail

Unknown Yahoo S5 Labeled Anomaly Detection Dataset (Yahoo Webscope S5) · training data not disclosed in enough detail

Unknown Numenta Anomaly Benchmark (NAB) · training data not disclosed in enough detail

Time-series classification

Unknown UCR Time Series Classification Archive (2018) · training data not disclosed in enough detail

Unknown UEA Multivariate Time Series Classification Archive (2018) · training data not disclosed in enough detail

Notes

Paper v4 (ICML 2024) Table 1 is the data composition; arXiv v1 described an earlier mix (Google Trends, M4, Electricity, Traffic, LibCity traffic; no Wiki, synthetic or Weather). GIFT-Eval's results config for TimesFM (model_link google/timesfm-1.0-200m) sets "testdata_leakage": "Yes". Only Electricity, Traffic and Weather among the long-horizon datasets are named as training data; ETTh1/2, ETTm1/2 explicitly held out; Exchange and ILI not mentioned. Other stated exclusions, not tracked here: Monash (18 datasets without missing values, Appendix A.5.2 Table 4) (all); Darts (8 datasets, Appendix A.5.1 Table 3) (all).

Sources