Speed of Time Series Classification Algorithms #6168
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@muriloasouza, that's an interesting question! We haven't done profiling or systematic experiments with datasets this large. I think this is an interesting development or research project, if you are interested in - just carrying out performance benchmarking for some of the more common algorithms. Further, if you have your own models - would you consider contributing them to |
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Hello @fkiraly, not really sure if this is the correct approach, but i tried this today:
Got this as result: If this is the correct approach, maybe tomorrow i can try increasing the range in samples. Or another algorithm maybe. One question regarding Deep Learning models, that we specify the number of epochs to train. Do you have any idea how this comparation can be done? How many epochs one should set when comparing to other algorithms that does not use epochs to train? |
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Greetings,
I am working in a multiclass TSC problem (12 classes in total). My dataset contains around 4 millions of rows (4 millions of timeseries) and 48 columns (half-hourly measurements for a single day). Consider a train/test split of 0.7/0.3. And again, split that train part into train/validation with a 0.8/0.2 split.
How fast the TSC algorithms (Dictionary-based, Distance-based or Shapelet-based) would be to solve this problem? For example, currently i have developed a LSTM model from Keras library, that took around 1,5 hour to train/test.
Usually, are these TSC algorithms faster or slower than those traditional Deep Learning models we find in Keras or other DL libraries? If they are slower, any other TSC algorithm could be faster?
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