Contamination MapTime series / Model
Granite-TimeSeries-TTM-R1
- Organisation
- IBM Research
- Family
- Tiny Time Mixers (TTM) · version R1
- Released
- 2024-04-05 (HF repo creation (createdAt) of ibm-granite/granite-timeseries-ttm-r1, https://huggingface.co/api/models/ibm-granite/granite-timeseries-ttm-r1)
- Checkpoints
- ibm-granite/granite-timeseries-ttm-r1, ibm-granite/granite-timeseries-ttm-r1@main (512-96), ibm-granite/granite-timeseries-ttm-r1@1024_96_v1 (1024-96)
- Weights
- Open · apache-2.0
- Links
- PaperModel cardCode
Training data disclosure: Full list
Level determined for 100% of benchmark cells.
- TestTest data seen0
- SeenSeen, split unclear4
- TrainTraining data seen0
- Related data seen0
- NoNot seen45
- ?Unknown0
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Other declared training data
-
Australian Electricity Demand · split: unknown
Evidence
“Australian Electricity Demand: https://zenodo.org/records/4659727”
huggingface.co · model card · Training Data section · retrieved 2026-10-15 -
Australian Weather · split: unknown
Zenodo 4654822 is the Monash 'Weather Dataset' (Australian daily weather), not the long-horizon Weather (Max Planck) dataset.
Evidence
“Australian Weather: https://zenodo.org/records/4654822”
huggingface.co · model card · Training Data section · retrieved 2026-10-15 -
Bitcoin dataset · split: unknown
Evidence
“Bitcoin dataset: https://zenodo.org/records/5122101”
huggingface.co · model card · Training Data section · retrieved 2026-10-15 -
KDD Cup 2018 · split: unknown
Evidence
“KDD Cup 2018 dataset: https://zenodo.org/records/4656756”
huggingface.co · model card · Training Data section · retrieved 2026-10-15 -
London Smart Meters · split: unknown
Evidence
“London Smart Meters: https://zenodo.org/records/4656091”
huggingface.co · model card · Training Data section · retrieved 2026-10-15 -
Saugeen River Flow · split: unknown
Evidence
“Saugeen River Flow: https://zenodo.org/records/4656058”
huggingface.co · model card · Training Data section · retrieved 2026-10-15 -
Solar Power · split: unknown
Evidence
“Solar Power: https://zenodo.org/records/4656027”
huggingface.co · model card · Training Data section · retrieved 2026-10-15 -
Sunspots · split: unknown
Evidence
“Sunspots: https://zenodo.org/records/4654722”
huggingface.co · model card · Training Data section · retrieved 2026-10-15 -
Solar (LSTNet, 137 Alabama PV plants) · split: unknown
Matched via the cited Zenodo record 4656144 (Monash Solar 10 Minutes, the 137 Alabama plants of Lai et al.).
Evidence
“Solar: https://zenodo.org/records/4656144”
huggingface.co · model card · Training Data section · retrieved 2026-10-15 -
US Births · split: unknown
Evidence
“US Births: https://zenodo.org/records/4656049”
huggingface.co · model card · Training Data section · retrieved 2026-10-15 -
Wind Farms Production data · split: unknown
Evidence
“Wind Farms Production data: https://zenodo.org/records/4654858”
huggingface.co · model card · Training Data section · retrieved 2026-10-15 -
Wind Power · split: unknown
Evidence
“Wind Power: https://zenodo.org/records/4656032”
huggingface.co · model card · Training Data section · retrieved 2026-10-15
Declared exclusions
-
Dataset Weather (Jena, 2020; Autoformer) · entirely excluded
Evidence
“Moreover, we remove all the datasets that we utilize for evaluation (i.e., Weather, Electricity, and Traffic).”
arxiv.org · paper · Section 4.1 · retrieved 2026-10-15 “Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
arxiv.org · paper · Table 8 caption / Appendix C.1.1 · retrieved 2026-10-15 “TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
huggingface.co · model card · Benchmarks section · retrieved 2026-10-15 -
Dataset Electricity (ECL, 321 clients) · entirely excluded
Evidence
“Moreover, we remove all the datasets that we utilize for evaluation (i.e., Weather, Electricity, and Traffic).”
arxiv.org · paper · Section 4.1 · retrieved 2026-10-15 “Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
arxiv.org · paper · Table 8 caption / Appendix C.1.1 · retrieved 2026-10-15 “TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
huggingface.co · model card · Benchmarks section · retrieved 2026-10-15 -
Dataset Traffic (PeMS San Francisco Bay, 862 sensors) · entirely excluded
Evidence
“Moreover, we remove all the datasets that we utilize for evaluation (i.e., Weather, Electricity, and Traffic).”
arxiv.org · paper · Section 4.1 · retrieved 2026-10-15 “Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
arxiv.org · paper · Table 8 caption / Appendix C.1.1 · retrieved 2026-10-15 “TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
huggingface.co · model card · Benchmarks section · retrieved 2026-10-15 -
Dataset ETTh1 · entirely excluded
Evidence
“Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
arxiv.org · paper · Table 8 caption / Appendix C.1.1 · retrieved 2026-10-15 “For zero/few-shot evaluation we consider seven public datasets (D1): ETTH1, ETTH2, ETTM1, ETTM2, Weather, Electricity, and Traffic”
arxiv.org · paper · Section 4.1 · retrieved 2026-10-15 “TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
huggingface.co · model card · Benchmarks section · retrieved 2026-10-15 -
Dataset ETTh2 · entirely excluded
Evidence
“Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
arxiv.org · paper · Table 8 caption / Appendix C.1.1 · retrieved 2026-10-15 “For zero/few-shot evaluation we consider seven public datasets (D1): ETTH1, ETTH2, ETTM1, ETTM2, Weather, Electricity, and Traffic”
arxiv.org · paper · Section 4.1 · retrieved 2026-10-15 “TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
huggingface.co · model card · Benchmarks section · retrieved 2026-10-15 -
Dataset ETTm1 · entirely excluded
Evidence
“Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
arxiv.org · paper · Table 8 caption / Appendix C.1.1 · retrieved 2026-10-15 “For zero/few-shot evaluation we consider seven public datasets (D1): ETTH1, ETTH2, ETTM1, ETTM2, Weather, Electricity, and Traffic”
arxiv.org · paper · Section 4.1 · retrieved 2026-10-15 “TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
huggingface.co · model card · Benchmarks section · retrieved 2026-10-15 -
Dataset ETTm2 · entirely excluded
Evidence
“Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
arxiv.org · paper · Table 8 caption / Appendix C.1.1 · retrieved 2026-10-15 “For zero/few-shot evaluation we consider seven public datasets (D1): ETTH1, ETTH2, ETTM1, ETTM2, Weather, Electricity, and Traffic”
arxiv.org · paper · Section 4.1 · retrieved 2026-10-15 “TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
huggingface.co · model card · Benchmarks section · retrieved 2026-10-15
Benchmark cells
GIFT-Eval
Not seen Jena Weather (GIFT-Eval) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen BizITObs - Application · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen BizITObs - Service · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen BizITObs - L2C · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen Bitbrains - Fast Storage · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen Bitbrains - rnd · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen Restaurant (Recruit Restaurant Visitor Forecasting) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen ETT1 (GIFT-Eval) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen ETT2 (GIFT-Eval) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen Loop Seattle (LibCity) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen SZ-Taxi (LibCity) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen M_DENSE (LibCity) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Seen, split unclear Solar (LSTNet, 137 Alabama PV plants) · the dataset was used; the source does not say which part
Evidence (1)
“Solar: https://zenodo.org/records/4656144”
Not seen Hierarchical Sales (Mancuso et al., 2020) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen M4 Yearly · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen M4 Quarterly · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen M4 Monthly · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen M4 Weekly · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen M4 Daily · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen M4 Hourly · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen Hospital · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen COVID Deaths · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Seen, split unclear US Births · the dataset was used; the source does not say which part
Evidence (1)
“US Births: https://zenodo.org/records/4656049”
Seen, split unclear Saugeen River Flow · the dataset was used; the source does not say which part
Evidence (1)
“Saugeen River Flow: https://zenodo.org/records/4656058”
Not seen Temperature Rain · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Seen, split unclear KDD Cup 2018 · the dataset was used; the source does not say which part
Evidence (1)
“KDD Cup 2018 dataset: https://zenodo.org/records/4656756”
Not seen Car Parts · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen ElectricityLoadDiagrams20112014 (UCI) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Long-horizon forecasting (ETT, Electricity, Traffic, Weather, Exchange, ILI)
Not seen ETTh1 · the authors state the dataset was excluded from training
Evidence (3)
“Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
“For zero/few-shot evaluation we consider seven public datasets (D1): ETTH1, ETTH2, ETTM1, ETTM2, Weather, Electricity, and Traffic”
“TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
Not seen ETTh2 · the authors state the dataset was excluded from training
Evidence (3)
“Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
“For zero/few-shot evaluation we consider seven public datasets (D1): ETTH1, ETTH2, ETTM1, ETTM2, Weather, Electricity, and Traffic”
“TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
Not seen ETTm1 · the authors state the dataset was excluded from training
Evidence (3)
“Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
“For zero/few-shot evaluation we consider seven public datasets (D1): ETTH1, ETTH2, ETTM1, ETTM2, Weather, Electricity, and Traffic”
“TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
Not seen ETTm2 · the authors state the dataset was excluded from training
Evidence (3)
“Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
“For zero/few-shot evaluation we consider seven public datasets (D1): ETTH1, ETTH2, ETTM1, ETTM2, Weather, Electricity, and Traffic”
“TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
Not seen Electricity (ECL, 321 clients) · the authors state the dataset was excluded from training
Evidence (3)
“Moreover, we remove all the datasets that we utilize for evaluation (i.e., Weather, Electricity, and Traffic).”
“Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
“TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
Not seen Traffic (PeMS San Francisco Bay, 862 sensors) · the authors state the dataset was excluded from training
Evidence (3)
“Moreover, we remove all the datasets that we utilize for evaluation (i.e., Weather, Electricity, and Traffic).”
“Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
“TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
Not seen Weather (Jena, 2020; Autoformer) · the authors state the dataset was excluded from training
Evidence (3)
“Moreover, we remove all the datasets that we utilize for evaluation (i.e., Weather, Electricity, and Traffic).”
“Please note that, these pre-training datasets have no overlap with the evaluation datasets.”
“TTM-Q referred in the paper maps to the `512-96` model uploaded in the main branch.”
Not seen Exchange-Rate · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen ILI (CDC influenza-like illness, national) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Time-series anomaly detection
Not seen TSB-AD-U (TSB-AD univariate) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen TSB-AD-M (TSB-AD multivariate) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen UCR Time Series Anomaly Archive (Hexagon ML/UCR, KDD Cup 2021) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen Server Machine Dataset (SMD) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen Mars Science Laboratory (MSL) telemetry anomaly dataset · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen Soil Moisture Active Passive (SMAP) telemetry anomaly dataset · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen Pooled Server Metrics (PSM) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen Secure Water Treatment (SWaT) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen Yahoo S5 Labeled Anomaly Detection Dataset (Yahoo Webscope S5) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen Numenta Anomaly Benchmark (NAB) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Time-series classification
Not seen UCR Time Series Classification Archive (2018) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Not seen UEA Multivariate Time Series Classification Archive (2018) · absent from the complete list of training data
Evidence (2)
“The original r1 TTM models were trained on a collection of datasets from the Monash Time Series Forecasting repository. The datasets used include:”
“TTM-R1 comprises TTM variants pre-trained on 250M public training samples.”
Notes
Main branch (512-96) = TTM-Q of the paper (Monash-only, ~250M samples, with diverse resolution sampling augmentation). Paper exclusions (Weather/Electricity/Traffic/ETT) attached via that mapping. GIFT-Eval leaderboard config for TTM-R1-Pretrained marks "testdata_leakage": "Yes".
Sources
- Granite-TimeSeries-TTM-R1 model card · model card · retrieved 2026-10-15
- Tiny Time Mixers (TTMs) (arXiv v8) · paper · retrieved 2026-10-15
- GIFT-Eval leaderboard config for TTM-R1-Pretrained · leaderboard · retrieved 2026-10-15