Category : time-series

After training N-BEATS model, I want to predict with an out of sample dataframe with the length of max_encoder_lentgh as an input, and should have an output of max_prediction_length. It throws "AssertionError: filters should not remove entries all entries – check encoder/decoder lengths and lags" if the length of the dataframe is lower than: max_encoder_length ..

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Suppose we have a dataframe including time indices and we want to extract only a dataframe including 10:23 to 14:34. How can we do this? n =1000 i = pd.date_range(‘2018-04-09′, periods=n, freq=’1min’) ts = pd.DataFrame({‘A’: [i for i in range(n)]}, index=i) print(ts) A 2018-04-09 00:00:00 0 2018-04-09 00:01:00 1 2018-04-09 00:02:00 2 2018-04-09 00:03:00 3 ..

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I have a timeseries of shape(n_samples,window_length,num_features) for binary classification. However I am unable to apply them in a time series problem because all the readings that I have got contains single feature dtw-knn. I have already gone through given link – How to use Dynamic Time warping with kNN in python Which has dynamic time ..

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