will it work for multivariate time series classification for example mixture of categorical and continues data?

markdregan / K-Nearest-Neighbors-with-Dynamic-Time-Warping

Python implementation of KNN and DTW classification algorithm

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will it work for multivariate time series classification for example mixture of categorical and continues data? #11

Open Sandy4321 opened 5 years ago

Sandy4321 commented 5 years ago

will it work for  multivariate  time series classification for example mixture of categorical and continues data? 
for example at time t1 we have observation: red, 2.4 , 5, 12.456 and time t2: green, 3.5, 2, 45.78; time t3: black, 5.6, 7, 23.56; t4: red, 2.1, 5, 12.6 ?

yuting1214 commented 4 years ago

In my opinion, I think this method doesn't work as you stated. In time series classification situation, it's obvious that the order of the features(or raw data) does matter in the dataset so that we can discover the pattern existed in data based on the innate sequential information. More importantly, it's difficult to determine the valid and practical sequence of a mixture of categorical and continuous data to fit in with DTW algorithm.