Comments (1)
Hi, chrischia06! Thanks for bringing up the question. I've noticed that you didn't wrap your time series in TimeSeriesData object from Kats.consts, which was causing failures when we call detectors to perform detections to get CUSUM, Bayesian Changepoint, Trend features. After wrapping the time series up into a TimeSeriesData object, you should see these features are not null any more. Here's a quick example: https://colab.research.google.com/drive/1XqBbcNcKW_5Boe35bACYff7Id9PHc3aV?usp=sharing
As for holt-winters features, this is due to that we apply box-cox transformation on data when getting these features via exponential smoothing in statsmodels, and this requires the data to be strictly positive (reference: https://stackoverflow.com/questions/50180988/valueerror-data-must-be-positive-boxcox-scipy), which is not true in the percentage changes in stock data.
Lemme know if there's any question, and I'll close this issue for now. Thanks!
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