I have some data that may or may not be called time series data, and I would like to be able to perform some machine learning classification on the data to obtain some general insights.
The data is of individual train journey's along some section of track and describes the speed, direction, geo location and many other measures. An example set of the data is shown below. (You will notice that each train journey is described by 150 - 250 rows, and I have highlighted each individual train journey). So you might say its a matrix of time series data with a hell of a lot of dimensions (more than visible).
I've found that the "Time series" analysis models are mostly around forecasting from existing data, but I would like to be able to essentially compare each train journey with each other to gain some insight into the performance of the trains and the behaviours of the drivers.
The main problem I have is that one train journey is described by a few hundred data points (rows). Any classification model assumes each row is a separate data point. So I basically want to treat each train as a single row for the classification, and still be able to classify the journey, or parts of the journey to identify problem areas on the track, or performance issues in trains.
My goal is to classify each train journey to attempt to gain some general insights about the train performance. However, to ask a more specific (and probably more helpful) question; 1. What areas of the track are trains underperforming/over-performing? ie, are trains under-performing on up hills, or down hills. 2. Why are drivers slowing down in some areas when there is no known reason (ie, no speed restrictions, no oncoming traffic, straight and level track)?
My question is, what machine learning models (preferably unsupervised) would be a good fit to classify my configuration of data?
I'm just starting to get my hands dirty with machine learning tools, I have a good level of knowledge of Python, C/C++/C#, and Matlab. So examples with any of these languages would be fine.