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Four Hugging Face families, 81 of the catalog's 117 models. The usual starting point.

extra CPU tag GPU tag families models example
hub :hub :hub-gpu Chronos Bolt, Chronos T5, TTM, TimesFM 2.x 81 chronos_bolt

Builds on base, so naive is available here too. Every extra that includes hub can load these models as well.

Start a server

docker run --rm -p 8000:8000 sktime/tserve:hub chronos_bolt

uv pip install "tserve[server,hub]"

uv run tserve chronos_bolt

pip install "tserve[server,hub]"

tserve chronos_bolt

Check what loaded:

curl -s http://127.0.0.1:8000/models

Predict

Python needs the client extra on the caller.

curl -s http://127.0.0.1:8000/predict -H "Content-Type: application/json" -d '{
  "past": {
    "timestamp": ["2024-01-01", "2024-01-02", "2024-01-03", "2024-01-04", "2024-01-05"],
    "sales": [120, 135, 128, 142, 138]
  },
  "time": "timestamp",
  "target": ["sales"],
  "fh": 3,
  "model": "chronos_bolt"
}'

curl.exe -s http://127.0.0.1:8000/predict -H "Content-Type: application/json" -d '{"past":{"timestamp":["2024-01-01","2024-01-02","2024-01-03","2024-01-04","2024-01-05"],"sales":[120,135,128,142,138]},"time":"timestamp","target":["sales"],"fh":3,"model":"chronos_bolt"}'

from tserve.client import Client

past = {
    "timestamp": ["2024-01-01", "2024-01-02", "2024-01-03", "2024-01-04", "2024-01-05"],
    "sales": [120, 135, 128, 142, 138],
}

with Client("http://127.0.0.1:8000") as client:
    result = client.predict(
        past=past,
        time="timestamp",
        target=["sales"],
        fh=3,
        model="chronos_bolt",
    )
print(result.predictions)

Models

Chronos Bolt

ChronosForecaster extra hub 4 models
multivariate exogenous quantiles
model checkpoint
chronos_bolt amazon/chronos-bolt-tiny
chronos_bolt_mini amazon/chronos-bolt-mini
chronos_bolt_small amazon/chronos-bolt-small
chronos_bolt_base amazon/chronos-bolt-base

Chronos T5

ChronosForecaster extra hub 5 models
multivariate exogenous quantiles

The original Chronos line.

model checkpoint
chronos_t5 amazon/chronos-t5-tiny
chronos_t5_mini amazon/chronos-t5-mini
chronos_t5_small amazon/chronos-t5-small
chronos_t5_base amazon/chronos-t5-base
chronos_t5_large amazon/chronos-t5-large

TTM

TinyTimeMixerForecaster extra hub 70 models
multivariate exogenous quantiles

IBM Granite Tiny Time Mixers. Models are {revision}-{context}-{horizon}, with optional -lite or -l1. The four short models instead take the forecaster default revision, and ttm its default repo too.

TTM defaults

model repo revision
ttm forecaster default (ibm/TTM) forecaster default (main)
ttm_r1 ibm-granite/granite-timeseries-ttm-r1 forecaster default (main)
ttm_r2 ibm-granite/granite-timeseries-ttm-r2 forecaster default (main)
ttm_r3 ibm-granite/granite-timeseries-ttm-r3 forecaster default (main)

TTM r1

ibm-granite/granite-timeseries-ttm-r1

model context horizon
ttm_r1_512_96 512 96
ttm_r1_1024_96 1024 96

TTM r2

ibm-granite/granite-timeseries-ttm-r2

model context horizon
ttm_r2_512_96 512 96
ttm_r2_512_192 512 192
ttm_r2_512_336 512 336
ttm_r2_512_720 512 720
ttm_r2_1024_96 1024 96
ttm_r2_1024_192 1024 192
ttm_r2_1024_336 1024 336
ttm_r2_1024_720 1024 720
ttm_r2_1536_96 1536 96
ttm_r2_1536_192 1536 192
ttm_r2_1536_336 1536 336
ttm_r2_1536_720 1536 720

TTM r2.1

Same Hub repo as r2. -l1 is the L1 checkpoint.

model context horizon variant
ttm_r2_1_52_16 52 16
ttm_r2_1_52_16_l1 52 16 L1
ttm_r2_1_90_30 90 30
ttm_r2_1_90_30_l1 90 30 L1
ttm_r2_1_180_60_l1 180 60 L1
ttm_r2_1_360_60_l1 360 60 L1
ttm_r2_1_512_48 512 48
ttm_r2_1_512_48_l1 512 48 L1
ttm_r2_1_512_96 512 96
ttm_r2_1_512_96_l1 512 96 L1

TTM r3

ibm-granite/granite-timeseries-ttm-r3. Each model has a -lite sibling.

model lite context horizon
ttm_r3_52_16 ttm_r3_52_16_lite 52 16
ttm_r3_90_30 ttm_r3_90_30_lite 90 30
ttm_r3_156_16 ttm_r3_156_16_lite 156 16
ttm_r3_180_60 ttm_r3_180_60_lite 180 60
ttm_r3_360_60 ttm_r3_360_60_lite 360 60
ttm_r3_512_30 ttm_r3_512_30_lite 512 30
ttm_r3_512_48 ttm_r3_512_48_lite 512 48
ttm_r3_512_96 ttm_r3_512_96_lite 512 96
ttm_r3_512_336 ttm_r3_512_336_lite 512 336
ttm_r3_768_48 ttm_r3_768_48_lite 768 48
ttm_r3_1024_48 ttm_r3_1024_48_lite 1024 48
ttm_r3_1024_96 ttm_r3_1024_96_lite 1024 96
ttm_r3_1024_720 ttm_r3_1024_720_lite 1024 720
ttm_r3_1536_96 ttm_r3_1536_96_lite 1536 96
ttm_r3_1536_720 ttm_r3_1536_720_lite 1536 720
ttm_r3_2048_96 ttm_r3_2048_96_lite 2048 96
ttm_r3_2048_720 ttm_r3_2048_720_lite 2048 720
ttm_r3_2560_96 ttm_r3_2560_96_lite 2560 96
ttm_r3_2560_720 ttm_r3_2560_720_lite 2560 720
ttm_r3_3072_96 ttm_r3_3072_96_lite 3072 96
ttm_r3_3072_720 ttm_r3_3072_720_lite 3072 720

TimesFM 2.x

TimesFM2Forecaster extra hub 2 models
multivariate exogenous quantiles

Quantile levels are the ones the checkpoint config ships.

model checkpoint
timesfm_2_5 google/timesfm-2.5-200m-transformers
timesfm_2 google/timesfm-2.0-500m-pytorch

Also loadable here

Next steps