Time Series classification problem (how to format data?) I am working on a project where a physical test over time is conducted to decide whether an object is diagnosed as class $A$ or class $B$. Typically these tests can take around 2.5-3 hours and so each timestep $t$ is recorded. $t$ is usually around one second long and so each row has a set of features at a particular second during the test. Once the test is completed, you decide whether the object is of type $A$ or $B$. Humans usually look at the time-series plot of the test to determine this classification - but I am tasked with automating it.
The issue is each CSV file for a test is approx 9000 to 11000 (2.5 to 3 multiplied by 3600 seconds in an hour) rows since the test can vary in time. The amount of features/columns are fixed. I have $N$ CSV files, which represent the time series data for one test done on one sample object (note, one sample object always gets tested once). So, my question: Is there a way to aggregate each sample so that I can have single dataframe to train my classifier on? Or is there another approach?
To add more clarity, I will have to make predictions on CSV files that come with inconsistent row dimensions due to the variance in testing time.
E.g.;

*

*csv1.shape = (8751, 1257) --> Prediction: Class $A$

*csv2.shape = (10321, 1257) --> Prediction: Class $A$

*csv3.shape = (9978, 1257) --> Prediction: Class $B$

One option would be calculating summary statistics (e.g. mean, min, max, autocorrelation of varying degrees etc.) from each time series for each feature and representing an object with a long set of features.
Another option is to feed the multivariate time series into an LSTM-like architecture and train with the raw series. Although note that making LSTMs work with time series of different length is possible but may need special attention.
