A new algorithm combines deep Gaussian process predictions, sliding-window spectral analysis, and an acquisition function that balances spectral change and uncertainty to detect change points while actively choosing where to sample.
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Active Learning for Multiple Change Point Detection in Non-stationary Time Series with Deep Gaussian Processes
A new algorithm combines deep Gaussian process predictions, sliding-window spectral analysis, and an acquisition function that balances spectral change and uncertainty to detect change points while actively choosing where to sample.