Classify multivariate time series 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.
If you're in Python, there are a couple of packages that can automatically extract hundreds or thousands of features from your timeseries, correlate them with your labels, choose the most significant, and train models for you.

https://github.com/blue-yonder/tsfresh

https://github.com/rtavenar/tslearn