I have a question on feature-extraction from time-series data. I have worked on time-series data before but my knowledge is limited to ARMA.
I read a few papers as well as thread here in the forum where the authors have extracted statistical features such as the count, mean, deviation, skewness and kurtosis, And used it for time series classification. I am trying to do the same but I don't understand the part about extracting statistical features of the entire time series.
What does it mean? Wouldn't the entire series have just 1 mean or std. deviation. How to incorporate the features data with the time-series?
Would appreciate the help if someone can simplify this whole concept. Thanks.
I have multiple time series, each series with 365 time period, a years worth of daily records for 100 different series. I performed feature extraction using
tsfresh package in python, the output was 100 rows, 1 for each of the series. Do i use these 100 rows as data for classification or should I merge it with the original time-series data?