Have some questions regarding multivariate time series classification.

I have some data. experimental data where there are three measurements measured at each time stems. Each is a univariate time series (Xposition, Yposition, roadoffset, brake)

And I need to predict the response, which takes 3 values.

It’s a classification problem, and i have read literature on the various methods. I have tried distance based methods, and decided against it because the distance matrix is too costly. I have got some code with sktime library on using the methods with convolution kernels (ROCKET, ARSENAL), and some ensemble methods (TimeSeriesForestClassifer).

However, I thought of a different method myself, where I split the data, into 2 second intervals, leading to 10 chunks of 2 second intervals of data. Each chunk is roughly 3000 samples.

Now that I have these 10 chunks of data, I wanted to extract some features from each of these 2 second intervals for my multivariate time series, and use these features in a classification model.

My hope is to do this so I can capture some temporal specific features. However, I don’t know what would be useful features to extract. What would be some useful features to extract from such a series?

I was hoping to get feature vectors from each of these intervals with my response in order to construct another classifier. Or are there methods that are out there they already do this?