MOMENT-1

Training data disclosure: Full list

Level determined for 75% of benchmark cells.

• TestTest data seen1
• SeenSeen, split unclear14
• TrainTraining data seen16
• Rel.Related data seen6
• NoNot seen0
• ?Unknown12

Training corpora

• Time Series Pile · 64 sources · pre-training · homepage
  Evidence that MOMENT-1 uses it

  “We call this collection the Time Series Pile.”

  “We use the Time Series Pile and these strategies to pre-train”

  “AutonLab/Timeseries-PILE”

  Contents of this corpus

  • ETTh1 · split: train of long-horizon

    MOMENT pre-trains only on the training split; the paper says it adheres to the predefined splits of each dataset's creators and splits long-horizon datasets horizontally (Fig. 2). The HF file is the full CSV.

    Evidence

    “2 hourly and minutely subsets of the Electricity Transformer Temperature (ETT)”

    “forecasting/autoformer/ETTh1.csv”

    “We only use the training splits of all datasets for pre-training.”

    “We adhere to the predefined splits provided by the creators of each dataset.”

  • ETTh2 · split: train of long-horizon

    MOMENT pre-trains only on the training split; the paper says it adheres to the predefined splits of each dataset's creators and splits long-horizon datasets horizontally (Fig. 2). The HF file is the full CSV.

    Evidence

    “2 hourly and minutely subsets of the Electricity Transformer Temperature (ETT)”

    “forecasting/autoformer/ETTh2.csv”

    “We only use the training splits of all datasets for pre-training.”

    “We adhere to the predefined splits provided by the creators of each dataset.”

  • ETTm1 · split: train of long-horizon

    MOMENT pre-trains only on the training split; the paper says it adheres to the predefined splits of each dataset's creators and splits long-horizon datasets horizontally (Fig. 2). The HF file is the full CSV.

    Evidence

    “2 hourly and minutely subsets of the Electricity Transformer Temperature (ETT)”

    “forecasting/autoformer/ETTm1.csv”

    “We only use the training splits of all datasets for pre-training.”

    “We adhere to the predefined splits provided by the creators of each dataset.”

  • ETTm2 · split: train of long-horizon

    MOMENT pre-trains only on the training split; the paper says it adheres to the predefined splits of each dataset's creators and splits long-horizon datasets horizontally (Fig. 2). The HF file is the full CSV.

    Evidence

    “2 hourly and minutely subsets of the Electricity Transformer Temperature (ETT)”

    “forecasting/autoformer/ETTm2.csv”

    “We only use the training splits of all datasets for pre-training.”

    “We adhere to the predefined splits provided by the creators of each dataset.”

  • Electricity (ECL, 321 clients) · split: train of long-horizon

    MOMENT pre-trains only on the training split; the paper says it adheres to the predefined splits of each dataset's creators and splits long-horizon datasets horizontally (Fig. 2). The HF file is the full CSV.

    Evidence

    “Electricity (Trindade, 2015)”

    “forecasting/autoformer/electricity.csv”

    “We only use the training splits of all datasets for pre-training.”

    “We adhere to the predefined splits provided by the creators of each dataset.”

  • Traffic (PeMS San Francisco Bay, 862 sensors) · split: train of long-horizon

    MOMENT pre-trains only on the training split; the paper says it adheres to the predefined splits of each dataset's creators and splits long-horizon datasets horizontally (Fig. 2). The HF file is the full CSV.

    Evidence

    “Traffic (California Department of Transportation, 2024)”

    “forecasting/autoformer/traffic.csv”

    “We only use the training splits of all datasets for pre-training.”

    “We adhere to the predefined splits provided by the creators of each dataset.”

  • Weather (Jena, 2020; Autoformer) · split: train of long-horizon

    MOMENT pre-trains only on the training split; the paper says it adheres to the predefined splits of each dataset's creators and splits long-horizon datasets horizontally (Fig. 2). The HF file is the full CSV.

    Evidence

    “Weather (Max Planck Institute for Biogeochemistry, 2024)”

    “forecasting/autoformer/weather.csv”

    “We only use the training splits of all datasets for pre-training.”

    “We adhere to the predefined splits provided by the creators of each dataset.”

  • ILI (CDC influenza-like illness, national) · split: train of long-horizon

    MOMENT pre-trains only on the training split; the paper says it adheres to the predefined splits of each dataset's creators and splits long-horizon datasets horizontally (Fig. 2). The HF file is the full CSV.

    Evidence

    “Influenza-like Illness (ILI) (Centers for Disease Control and Prevention, 2024)”

    “forecasting/autoformer/national_illness.csv”

    “We only use the training splits of all datasets for pre-training.”

    “We adhere to the predefined splits provided by the creators of each dataset.”

  • Exchange-Rate · split: train of long-horizon

    MOMENT pre-trains only on the training split; the paper says it adheres to the predefined splits of each dataset's creators and splits long-horizon datasets horizontally (Fig. 2). The HF file is the full CSV.

    Evidence

    “Exchange-rate (Lai et al., 2018)”

    “forecasting/autoformer/exchange_rate.csv”

    “We only use the training splits of all datasets for pre-training.”

    “We adhere to the predefined splits provided by the creators of each dataset.”

  • forecasting/monash/australian_electricity_demand_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/australian_electricity_demand_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/bitcoin_dataset_without_missing_values.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/bitcoin_dataset_without_missing_values.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • Car Parts · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/car_parts_dataset_without_missing_values.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/cif_2016_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/cif_2016_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • COVID Deaths · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/covid_deaths_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/dominick_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/dominick_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • Electricity (ECL, 321 clients) · split: train

    Same source data as long-horizon Electricity (321 clients). Short-horizon (Monash) datasets are split by whole series, so complete series in the training split are seen including the period used as the long-horizon test window.

    Evidence

    “forecasting/monash/electricity_hourly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • Electricity Weekly (Monash) · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/electricity_weekly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/fred_md_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/fred_md_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • Hospital · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/hospital_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/kaggle_web_traffic_dataset_without_missing_values.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/kaggle_web_traffic_dataset_without_missing_values.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/kaggle_web_traffic_weekly_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/kaggle_web_traffic_weekly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • KDD Cup 2018 · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/kdd_cup_2018_dataset_without_missing_values.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/london_smart_meters_dataset_without_missing_values.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/london_smart_meters_dataset_without_missing_values.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/m1_monthly_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m1_monthly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/m1_quarterly_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m1_quarterly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/m1_yearly_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m1_yearly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/m3_monthly_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m3_monthly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/m3_other_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m3_other_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/m3_quarterly_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m3_quarterly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/m3_yearly_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m3_yearly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • M4 Daily · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m4_daily_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • M4 Hourly · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m4_hourly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • M4 Monthly · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m4_monthly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • M4 Quarterly · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m4_quarterly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • M4 Weekly · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m4_weekly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • M4 Yearly · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/m4_yearly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/nn5_daily_dataset_without_missing_values.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/nn5_daily_dataset_without_missing_values.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/nn5_weekly_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/nn5_weekly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/pedestrian_counts_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/pedestrian_counts_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/rideshare_dataset_without_missing_values.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/rideshare_dataset_without_missing_values.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • Saugeen River Flow · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/saugeenday_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • Solar (LSTNet, 137 Alabama PV plants) · split: train

    Monash solar 10-minute (Alabama 2006).

    Evidence

    “forecasting/monash/solar_10_minutes_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/solar_4_seconds_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/solar_4_seconds_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • NREL Solar Power Data for Integration Studies (2006) · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/solar_weekly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/sunspot_dataset_without_missing_values.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/sunspot_dataset_without_missing_values.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • Temperature Rain · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/temperature_rain_dataset_without_missing_values.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/tourism_monthly_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/tourism_monthly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/tourism_quarterly_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/tourism_quarterly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/tourism_yearly_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/tourism_yearly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • Traffic (PeMS San Francisco Bay, 862 sensors) · split: train

    Same source data as long-horizon Traffic (862 sensors). Short-horizon (Monash) datasets are split by whole series, so complete series in the training split are seen including the period used as the long-horizon test window.

    Evidence

    “forecasting/monash/traffic_hourly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • Traffic Weekly (Monash) · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/traffic_weekly_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • US Births · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/us_births_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/vehicle_trips_dataset_without_missing_values.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/vehicle_trips_dataset_without_missing_values.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/weather_dataset.tsf · split: train

    Monash weather (Australian BOM), not the Jena long-horizon Weather dataset.

    Evidence

    “forecasting/monash/weather_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/wind_4_seconds_dataset.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/wind_4_seconds_dataset.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • forecasting/monash/wind_farms_minutely_dataset_without_missing_values.tsf · split: train

    Monash archive file in the Time Series Pile; split by whole series (60/10/30 random split when no predefined split).

    Evidence

    “forecasting/monash/wind_farms_minutely_dataset_without_missing_values.tsf”

    “We only use the training splits of all datasets for pre-training.”

    “For these datasets, a complete time series is either training, validation, or testing.”

  • UCR Time Series Classification Archive (2018) · split: train of ts-classification

    The HF repo folder classification/UCR/ holds 158 dataset folders = the 128 UCR 2018 univariate datasets plus all 30 UEA datasets, each with both _TRAIN.ts and _TEST.ts files. MOMENT states it adheres to the creators' predefined splits and pre-trains only on training splits, i.e. the official UCR TRAIN files. The entry covers two registry datasets (ucr-archive and uea-archive), hence two updates for this raw_name. MOMENT then evaluates classification on the TEST splits of 91 UCR datasets (unsupervised representations + SVM).

    Evidence

    “UCR/UEA classification archive (Dau et al., 2018) comprises of 159 time series datasets”

    “We only use the training splits of all datasets for pre-training.”

    “Table 16: Classification datasets in Time Series Pile taken from (Ismail Fawaz et al., 2019) [UCR Archive].”

    “We adhere to the predefined splits provided by the creators of each dataset.”

    “For these datasets, a complete time series is either training, validation, or testing.”

    “classification/UCR/ACSF1/ACSF1_TRAIN.ts”

  • UEA Multivariate Time Series Classification Archive (2018) · split: train of ts-classification

    All 30 UEA 2018 multivariate datasets (ArticularyWordRecognition ... UWaveGestureLibrary, incl. EigenWorms, InsectWingbeat, MotorImagery, PEMS-SF) are present as folders under classification/UCR/ with _TRAIN.ts and _TEST.ts files (checked against the HF file listing). Only the TRAIN files are used per the paper; MOMENT is univariate, so channels are presumably treated separately (not stated).

    Evidence

    “classification/UCR/MotorImagery/MotorImagery_TRAIN.ts”

    “classification/UCR/SpokenArabicDigits/SpokenArabicDigits_TRAIN.ts”

    “classification/UCR/InsectWingbeat/InsectWingbeat_TRAIN.ts”

    “UCR/UEA classification archive (Dau et al., 2018) comprises of 159 time series datasets”

    “We only use the training splits of all datasets for pre-training.”

    “We adhere to the predefined splits provided by the creators of each dataset.”

  • UCR Time Series Anomaly Archive (Hexagon ML/UCR, KDD Cup 2021) · split: train of ts-anomaly

    TSB-UAD's 'KDD21' folder holds all 250 series of the UCR/KDD Cup 2021 anomaly archive (files 001-250). Each file name encodes the creators' training-end index (e.g. 35000); MOMENT says it uses creators' splits and only training splits for pre-training, so the anomaly-free training prefix of every series was seen in pre-training, not the test part. Caveat: the pre-training data pipeline is not public; the released fine-tuning/eval loader (momentfm) uses exactly this file-name split. MOMENT reports anomaly detection on subsets (44 / 248 series) of this archive.

    Evidence

    “TSB-UAD anomaly benchmark (Paparrizos et al., 2022b) is a recent collection of 1980 univariate time series with labeled anomalies from 18 anomaly detection datasets”

    “We only use the training splits of all datasets for pre-training.”

    “Dodger ECG IOPS KDD21 MGAB NAB SensorScope YAHOO NASA-MSL NASA-SMAP Daphnet GHL Genesis MITDB OPP Occupancy SMD SVDB”

    “Table 12. Anomaly detection datasets in Time Series Pile taken from (Paparrizos et al., 2022b).”

    “anomaly_detection/TSB-UAD-Public/KDD21/001_UCR_Anomaly_DISTORTED1sddb40_35000_52000_52620.out”

    “anomaly_detection/TSB-UAD-Public/KDD21/250_UCR_Anomaly_weallwalk_2951_7290_7296.out”

    “We adhere to the predefined splits provided by the creators of each dataset.”

    “Long-horizon forecasting and anomaly detection datasets are typically long time series, which are split horizontally as shown in Fig. 2.”

    “thus ensuring that MOMENT only observes the training splits of datasets during pre-training.”

    “n_train = int(details[4])”

  • Server Machine Dataset (SMD) · split: test

    IMPORTANT: the 281 TSB-UAD SMD files are single channels taken from SMD's machine-*.test.csv files (file names 'machine-X-Y.test.csv@k.out'), i.e. they come from the official SMD TEST period (the only labelled part). MOMENT pre-trains on the training portion of each such series (horizontal split; whether TSB-UAD-provided or its own 60/10/30 split is not stated), so a prefix of the official SMD test data is in pre-training. Univariate per-channel versions, not the 38-dim multivariate SMD. TimeRCD (third party) excluded MOMENT results on SMD for this reason. TSB-UAD (used here) is the 2022 predecessor of TSB-AD; TSB-AD-U/-M were curated from many of the same sources (e.g. YAHOO, NAB, UCR, MSL, SMAP, SMD, IOPS, MGAB), so overlap with tsb-ad-u/tsb-ad-m series is likely but not established by the sources cited here.

    Evidence

    “Dodger ECG IOPS KDD21 MGAB NAB SensorScope YAHOO NASA-MSL NASA-SMAP Daphnet GHL Genesis MITDB OPP Occupancy SMD SVDB”

    “Table 12. Anomaly detection datasets in Time Series Pile taken from (Paparrizos et al., 2022b).”

    “anomaly_detection/TSB-UAD-Public/SMD/machine-1-1.test.csv@1.out”

    “Long-horizon forecasting and anomaly detection datasets are typically long time series, which are split horizontally as shown in Fig. 2.”

    “In cases where such splits are unavailable, we randomly sample 60% of the data for training, 10% for validation, and 30% for testing.”

    “thus ensuring that MOMENT only observes the training splits of datasets during pre-training.”

    “MOMENT: Uses the “Time Series Pile,” which contains IOPS, MGAB, NAB, YAHOO, MSL, SMAP, and SMD (Goswami et al., 2024).”

    “We exclude certain baseline results (specifically from DADA and MOMENT) because their pre-training corpora heavily overlap with our zero-shot test benchmarks, violating the zero-shot evaluation setting.”

  • Mars Science Laboratory (MSL) telemetry anomaly dataset · split: train of ts-anomaly

    TSB-UAD's NASA-MSL folder holds 54 files = 27 channels x (.train.out, .test.out). MOMENT says it follows creators' splits and pre-trains only on training splits, but does not say whether it used the .train.out files or a horizontal split of the .test.out files; hence split_of null. Univariate (first-channel) versions as in TSB-UAD, not the 55-dim multivariate MSL. TSB-UAD (used here) is the 2022 predecessor of TSB-AD; TSB-AD-U/-M were curated from many of the same sources (e.g. YAHOO, NAB, UCR, MSL, SMAP, SMD, IOPS, MGAB), so overlap with tsb-ad-u/tsb-ad-m series is likely but not established by the sources cited here.

    Evidence

    “Dodger ECG IOPS KDD21 MGAB NAB SensorScope YAHOO NASA-MSL NASA-SMAP Daphnet GHL Genesis MITDB OPP Occupancy SMD SVDB”

    “Table 12. Anomaly detection datasets in Time Series Pile taken from (Paparrizos et al., 2022b).”

    “anomaly_detection/TSB-UAD-Public/NASA-MSL/C-1.train.out”

    “anomaly_detection/TSB-UAD-Public/NASA-MSL/C-1.test.out”

    “We adhere to the predefined splits provided by the creators of each dataset.”

    “Long-horizon forecasting and anomaly detection datasets are typically long time series, which are split horizontally as shown in Fig. 2.”

    “thus ensuring that MOMENT only observes the training splits of datasets during pre-training.”

    “MOMENT: Uses the “Time Series Pile,” which contains IOPS, MGAB, NAB, YAHOO, MSL, SMAP, and SMD (Goswami et al., 2024).”

    “We exclude certain baseline results (specifically from DADA and MOMENT) because their pre-training corpora heavily overlap with our zero-shot test benchmarks, violating the zero-shot evaluation setting.”

  • Soil Moisture Active Passive (SMAP) telemetry anomaly dataset · split: train of ts-anomaly

    TSB-UAD's NASA-SMAP folder holds 108 files = 54 channels x (.train.out, .test.out). Which file/portion MOMENT used as training split is not stated (creators' split claimed). Univariate versions. TSB-UAD (used here) is the 2022 predecessor of TSB-AD; TSB-AD-U/-M were curated from many of the same sources (e.g. YAHOO, NAB, UCR, MSL, SMAP, SMD, IOPS, MGAB), so overlap with tsb-ad-u/tsb-ad-m series is likely but not established by the sources cited here.

    Evidence

    “Dodger ECG IOPS KDD21 MGAB NAB SensorScope YAHOO NASA-MSL NASA-SMAP Daphnet GHL Genesis MITDB OPP Occupancy SMD SVDB”

    “Table 12. Anomaly detection datasets in Time Series Pile taken from (Paparrizos et al., 2022b).”

    “anomaly_detection/TSB-UAD-Public/NASA-SMAP/A-1.train.out”

    “anomaly_detection/TSB-UAD-Public/NASA-SMAP/A-1.test.out”

    “We adhere to the predefined splits provided by the creators of each dataset.”

    “Long-horizon forecasting and anomaly detection datasets are typically long time series, which are split horizontally as shown in Fig. 2.”

    “thus ensuring that MOMENT only observes the training splits of datasets during pre-training.”

    “MOMENT: Uses the “Time Series Pile,” which contains IOPS, MGAB, NAB, YAHOO, MSL, SMAP, and SMD (Goswami et al., 2024).”

    “We exclude certain baseline results (specifically from DADA and MOMENT) because their pre-training corpora heavily overlap with our zero-shot test benchmarks, violating the zero-shot evaluation setting.”

  • Yahoo S5 Labeled Anomaly Detection Dataset (Yahoo Webscope S5) · split: train of ts-anomaly

    367 YAHOO files (A1real 67 + A2synthetic 100 + A3/A4Benchmark 200) = all of Yahoo S5. Yahoo has no creator train/test split, so per MOMENT the series are split horizontally 60/10/30 and the first ~60% (training part) of every series is in pre-training; that portion contains labelled anomalies used by benchmarks that score the whole series (e.g. TSB-AD). TSB-UAD (used here) is the 2022 predecessor of TSB-AD; TSB-AD-U/-M were curated from many of the same sources (e.g. YAHOO, NAB, UCR, MSL, SMAP, SMD, IOPS, MGAB), so overlap with tsb-ad-u/tsb-ad-m series is likely but not established by the sources cited here.

    Evidence

    “Dodger ECG IOPS KDD21 MGAB NAB SensorScope YAHOO NASA-MSL NASA-SMAP Daphnet GHL Genesis MITDB OPP Occupancy SMD SVDB”

    “Table 12. Anomaly detection datasets in Time Series Pile taken from (Paparrizos et al., 2022b).”

    “anomaly_detection/TSB-UAD-Public/YAHOO/Yahoo_A1real_1_data.out”

    “anomaly_detection/TSB-UAD-Public/YAHOO/YahooA3Benchmark-TS100_data.out”

    “Long-horizon forecasting and anomaly detection datasets are typically long time series, which are split horizontally as shown in Fig. 2.”

    “In cases where such splits are unavailable, we randomly sample 60% of the data for training, 10% for validation, and 30% for testing.”

    “thus ensuring that MOMENT only observes the training splits of datasets during pre-training.”

    “MOMENT: Uses the “Time Series Pile,” which contains IOPS, MGAB, NAB, YAHOO, MSL, SMAP, and SMD (Goswami et al., 2024).”

    “We exclude certain baseline results (specifically from DADA and MOMENT) because their pre-training corpora heavily overlap with our zero-shot test benchmarks, violating the zero-shot evaluation setting.”

  • Numenta Anomaly Benchmark (NAB) · split: train of ts-anomaly

    58 NAB files in TSB-UAD-Public/NAB. NAB has no creator train/test split, so per MOMENT a horizontal 60/10/30 split applies and the first ~60% of each series is in pre-training. TSB-UAD (used here) is the 2022 predecessor of TSB-AD; TSB-AD-U/-M were curated from many of the same sources (e.g. YAHOO, NAB, UCR, MSL, SMAP, SMD, IOPS, MGAB), so overlap with tsb-ad-u/tsb-ad-m series is likely but not established by the sources cited here.

    Evidence

    “Dodger ECG IOPS KDD21 MGAB NAB SensorScope YAHOO NASA-MSL NASA-SMAP Daphnet GHL Genesis MITDB OPP Occupancy SMD SVDB”

    “Table 12. Anomaly detection datasets in Time Series Pile taken from (Paparrizos et al., 2022b).”

    “anomaly_detection/TSB-UAD-Public/NAB/NAB_data_CloudWatch_1.out”

    “anomaly_detection/TSB-UAD-Public/NAB/NAB_data_tweets_9.out”

    “Long-horizon forecasting and anomaly detection datasets are typically long time series, which are split horizontally as shown in Fig. 2.”

    “In cases where such splits are unavailable, we randomly sample 60% of the data for training, 10% for validation, and 30% for testing.”

    “thus ensuring that MOMENT only observes the training splits of datasets during pre-training.”

    “MOMENT: Uses the “Time Series Pile,” which contains IOPS, MGAB, NAB, YAHOO, MSL, SMAP, and SMD (Goswami et al., 2024).”

    “We exclude certain baseline results (specifically from DADA and MOMENT) because their pre-training corpora heavily overlap with our zero-shot test benchmarks, violating the zero-shot evaluation setting.”

Declared exclusions

• Benchmark Long-horizon forecasting (ETT, Electricity, Traffic, Weather, Exchange, ILI) · test portion excluded
  Evidence

  “To avoid data contamination, we carefully partition all datasets into disjoint train, validation, and test splits.”

  “We only use the training splits of all datasets for pre-training.”

  “thus ensuring that MOMENT only observes the training splits of datasets during pre-training.”

Benchmark cells

GIFT-Eval

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

Time-series anomaly detection

Time-series classification

Notes

All three sizes are pre-trained on the Time Series Pile (training splits only, seed 13). No synthetic data. No newer MOMENT release found as of 2026-10-15.

Sources

• MOMENT: A Family of Open Time-series Foundation Models (arXiv v3) · paper · retrieved 2026-10-15
• MOMENT-1-large model card · model card · retrieved 2026-10-15
• Time Series PILE dataset card · dataset card · retrieved 2026-10-15