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ritulk
/
patchtst-model-based-on-occupancy-weather-data-time-series

Time Series Forecasting
Transformers
English
time-series
forecasting
passenger_occupancy
airport
weather
patchtst
transformer
cooling-load-prediction
multi-output
Model card Files Files and versions
xet
Community

Instructions to use ritulk/patchtst-model-based-on-occupancy-weather-data-time-series with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use ritulk/patchtst-model-based-on-occupancy-weather-data-time-series with Transformers:

    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoModel
    model = AutoModel.from_pretrained("ritulk/patchtst-model-based-on-occupancy-weather-data-time-series", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
patchtst-model-based-on-occupancy-weather-data-time-series
8.34 MB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 3 commits
ritulk's picture
ritulk
README.md
d7f6421 verified over 1 year ago
  • .gitattributes
    1.52 kB
    initial commit over 1 year ago
  • README.md
    272 Bytes
    README.md over 1 year ago
  • patchtst_occupancy_cooling_model.pkl
    8.34 MB
    xet
    Upload PatchTST model fine-tuned on occupancy+weather data via TSai. over 1 year ago