Time Series Forecasting
Transformers
PyTorch
Safetensors
patchtsmixer
time series
forecasting
pretrained models
foundation models
time series foundation models
time-series
Instructions to use ibm-granite/granite-timeseries-patchtsmixer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ibm-granite/granite-timeseries-patchtsmixer with Transformers:
# Load model directly from transformers import AutoTokenizer, PatchTSMixerForPrediction tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-timeseries-patchtsmixer") model = PatchTSMixerForPrediction.from_pretrained("ibm-granite/granite-timeseries-patchtsmixer") - Notebooks
- Google Colab
- Kaggle
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license: apache-2.0
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metrics:
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- mse
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---
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# PatchTSMixer model pre-trained on ETTh1 dataset
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license: apache-2.0
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metrics:
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- mse
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pipeline_tag: time-series-forecasting
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tags:
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- time series
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- forecasting
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- pretrained models
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- foundation models
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- time series foundation models
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- time-series
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---
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# PatchTSMixer model pre-trained on ETTh1 dataset
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