Contamination Map Methodology

Time series / Model

Time-MoE

Organisation
Xiaohongshu Inc., Princeton University, Squirrel Ai Learning, Griffith University
Family
Time-MoE · version Time-MoE (ICLR 2025)
Released
2024-09-24 (arXiv v1 submission date, https://arxiv.org/abs/2409.16040 (HF repos created 2024-09-21; README says weights made available Oct 2024))
Checkpoints
Maple728/TimeMoE-50M, Maple728/TimeMoE-200M
Weights
Open · Apache-2.0
Links
PaperModel cardCode

Training data disclosure: Full list

Level determined for 57% of benchmark cells.

“Table 10: Datasets and key properties from Time-300B.”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15

Training corpora

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

Seen, split unclear Restaurant (Recruit Restaurant Visitor Forecasting) · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“Restaurant”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

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

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

Seen, split unclear Loop Seattle (LibCity) · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“Loop Seattle”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear SZ-Taxi (LibCity) · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“SZ-Taxi”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear M_DENSE (LibCity) · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“other/M_DENSE/meta.json”
huggingface.co · website · HF repo file listing (API) · retrieved 2026-10-15

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

Seen, split unclear Hierarchical Sales (Mancuso et al., 2020) · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“Hierarchical Sales”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear M4 Yearly · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“M4 Yearly”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear M4 Quarterly · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“M4 Quarterly”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear M4 Monthly · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“M4 Monthly”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear M4 Weekly · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“M4 Weekly”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear M4 Daily · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“M4 Daily”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear M4 Hourly · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“M4 Hourly”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear Hospital · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“Hospital”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear COVID Deaths · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“COVID Deaths”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear US Births · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“US Births”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear Saugeen River Flow · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“Saugeen”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear Temperature Rain · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“Temperature Rain”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear KDD Cup 2018 · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“KDD Cup 2018”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear Car Parts · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“Car Parts”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Seen, split unclear ElectricityLoadDiagrams20112014 (UCI) · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“Electricity (15 min) Energy 15T 347 39,708,170 Godahewa et al. (2021)”
arxiv.org · paper · Appendix C, Table 10 · 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)
“we conducted experiments on the six well-known long-term forecasting benchmarks for which datasets were not included in the pre-training corpora.”
arxiv.org · paper · Section 4.1 Zero-shot forecasting · retrieved 2026-10-15
“including the Weather (Wu et al., 2021), Global Temp (Wu et al., 2023b), and ETT datasets (ETTh1, ETTh2, ETTm1, ETTm2) (Zhou et al., 2021).”
arxiv.org · paper · Appendix B, Benchmark Details · retrieved 2026-10-15

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

Evidence (2)
“we conducted experiments on the six well-known long-term forecasting benchmarks for which datasets were not included in the pre-training corpora.”
arxiv.org · paper · Section 4.1 Zero-shot forecasting · retrieved 2026-10-15
“including the Weather (Wu et al., 2021), Global Temp (Wu et al., 2023b), and ETT datasets (ETTh1, ETTh2, ETTm1, ETTm2) (Zhou et al., 2021).”
arxiv.org · paper · Appendix B, Benchmark Details · retrieved 2026-10-15

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

Evidence (2)
“we conducted experiments on the six well-known long-term forecasting benchmarks for which datasets were not included in the pre-training corpora.”
arxiv.org · paper · Section 4.1 Zero-shot forecasting · retrieved 2026-10-15
“including the Weather (Wu et al., 2021), Global Temp (Wu et al., 2023b), and ETT datasets (ETTh1, ETTh2, ETTm1, ETTm2) (Zhou et al., 2021).”
arxiv.org · paper · Appendix B, Benchmark Details · retrieved 2026-10-15

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

Evidence (2)
“we conducted experiments on the six well-known long-term forecasting benchmarks for which datasets were not included in the pre-training corpora.”
arxiv.org · paper · Section 4.1 Zero-shot forecasting · retrieved 2026-10-15
“including the Weather (Wu et al., 2021), Global Temp (Wu et al., 2023b), and ETT datasets (ETTh1, ETTh2, ETTm1, ETTm2) (Zhou et al., 2021).”
arxiv.org · paper · Appendix B, Benchmark Details · retrieved 2026-10-15

Related data seen Electricity (ECL, 321 clients) · related dataset ElectricityLoadDiagrams20112014 (UCI) was used (via Time-300B)

Evidence (6)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“Electricity (15 min) Energy 15T 347 39,708,170 Godahewa et al. (2021)”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15
“The raw dataset is in https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014.”
raw.githubusercontent.com · readme · Electricity consumption · retrieved 2026-10-15
“Because the some dimensions are equal to 0. So we eliminate the records in 2011. Final we get data contains electircity consumption of 321 clients from 2012 to 2014.”
raw.githubusercontent.com · readme · Electricity consumption · retrieved 2026-10-15
“https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014”
arxiv.org · paper · Section 4, footnote 1 · 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 (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“Traffic Hourly”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Not seen Weather (Jena, 2020; Autoformer) · the authors state the dataset was excluded from training

Evidence (2)
“we conducted experiments on the six well-known long-term forecasting benchmarks for which datasets were not included in the pre-training corpora.”
arxiv.org · paper · Section 4.1 Zero-shot forecasting · retrieved 2026-10-15
“including the Weather (Wu et al., 2021), Global Temp (Wu et al., 2023b), and ETT datasets (ETTh1, ETTh2, ETTm1, ETTm2) (Zhou et al., 2021).”
arxiv.org · paper · Appendix B, Benchmark Details · retrieved 2026-10-15

Seen, split unclear Exchange-Rate · the dataset was used; the source does not say which part (via Time-300B)

Evidence (3)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“Exchange Rate”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15

Related data seen ILI (CDC influenza-like illness, national) · related dataset CDC FluView ILINet (LOTSA) was used (via Time-300B)

Evidence (6)
“We pre-trained these models on our newly introduced large-scale data Time-300B”
arxiv.org · paper · Abstract · retrieved 2026-10-15
“During training, we utilized approximately 117 billion time points in Time-300B, sampling each batch according to fixed proportions of domains and distributions of observation values.”
arxiv.org · paper · Appendix C · retrieved 2026-10-15
“CDC Fluview ILINet”
arxiv.org · paper · Appendix C, Table 10 · retrieved 2026-10-15
“includes the weekly recorded influenza-like illness (ILI) patients data from Centers for Disease Control and Prevention of the United States between 2002 and 2021”
arxiv.org · paper · Section 4 (Datasets) · retrieved 2026-10-15
“https://gis.cdc.gov/grasp/fluview/fluportaldashboard.html”
arxiv.org · paper · Section 4, footnote 4 · retrieved 2026-10-15
“CDC Fluview ILINet CDC Fluview WHO NREVSS”
arxiv.org · paper · Table 14 (dataset column) · retrieved 2026-10-15

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

Not a THUML model (included in this scope; Time-300B overlaps heavily with UTSD/LOTSA sources). Paper trains Base (50M), Large (200M) and Ultra (2.4B); only 50M and 200M weights are on HF. Time-300B includes Electricity (15 min), Traffic, Traffic Hourly, Exchange Rate and CDC Fluview ILINet -- raw sources of the long-horizon ECL, Traffic, Exchange and ILI benchmarks; Time-MoE is accordingly not evaluated on ECL by Timer-XL/Sundial. Appendix text says 'eight well-established datasets' but lists six. Other stated exclusions, not tracked here: Global Temp (all).

Sources