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I have a set of data composed of time series (8 points) with about 40 dimensions (so each time series is 8 by 40). The corresponding ouput (the possible outcomes for the categories ) is eitheir 0 or 1.

**What would be the best approach to design a classifier for time series with multiple dimensions ?**

My initial strategy was to extract features from those time series : mean, std, maximum variation for each dimension. I obtained a dataset which I used to train a RandomTreeForest. Being aware of the total naivety of this, and after obtaining poor results, I am now looking for a more improved model.

My leads are the following : classify the series for each dimension (using KNN algorithm and DWT), reduce the dimensionality with PCA and use a final classifier along the multidimensions categories. Being relatively new to ML, I don't know if I am totally wrong.

What you are doing is a pretty good approach. How many samples do you have in your dataset? – Kasra Manshaei – 2017-05-09T09:11:18.457

I have about 500 000 time series (recalling that each time series is 8 timestamp * 40 dimensions ) – AugBar – 2017-05-09T09:17:17.947

Have you tried just using the 320 features raw? 320 features is not a lot for 500,000 samples – Jan van der Vegt – 2017-05-09T09:41:51.850

@Jan van der Vegt : I have tried that method using a neural network, but the results were not so convincing - i used the raw data without any pre-processing. What operations should I apply beforehand on my 320-features raws to feed the classifier ? – AugBar – 2017-05-09T10:50:59.167

1In case of a neural network normalizing your input is important, depending on the range of your features that might matter. But I would just feed the raw features into a RF and see how well that works, requires less tuning to see if you can get anything out of it easily – Jan van der Vegt – 2017-05-09T11:05:46.040