mims-harvard/Raincoat
Domain Adaptation for Time Series Under Feature and Label Shifts
When you have time series data from one context (like activity sensor data from one group of people) and need to apply a machine learning model to similar data from a different context (like sensor data from a new group, or a different sensor type), Raincoat helps you adapt that model. It takes your pre-trained model and time series data from both your original and new contexts to produce a more accurate model for the new situation, even if the patterns or categories of events have changed.
133 stars. No commits in the last 6 months.
Use this if you need to apply a time series classification model to new data where the underlying features (how the data looks) or the labels (what the data represents) have subtly or significantly shifted from your original training data.
Not ideal if your data is not time series, if you don't have a pre-trained model, or if you're looking for a simple plug-and-play solution without any configuration.
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133
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Jupyter Notebook
License
MIT
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Last pushed
Jun 21, 2023
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