White-Link/UnsupervisedScalableRepresentationLearningTimeSeries
Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments
This project helps data scientists and machine learning engineers analyze complex time-series data without needing extensive labeled examples. It takes raw, multivariate time-series datasets and learns meaningful representations, which can then be used to classify or understand patterns. The end result is a more efficient way to extract insights from large, unlabeled time-series datasets.
406 stars. No commits in the last 6 months.
Use this if you need to classify or understand patterns in large, multivariate time-series datasets where obtaining many labeled examples is difficult or impossible.
Not ideal if your time series data is simple, already well-labeled, or if you require very quick, off-the-shelf solutions without custom model training.
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406
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Jul 31, 2024
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