This is probably a simple question. Assume I'm interested in modelling a binary variable, with various covariates, including ones that are time series observations. In the usual modelling approach, one can try searching for various features from the timeseries data, such as standard deviations, averages, max and etc, to make a flat model matrix.
My question: what are the tools/approaches that allow for a (relatively) simple inclusion of time series data to a classification problem?
I don't think panel regression would work since the time series data is very different among the rows, sometimes is very sparse and asynchronous. Melting the data, due to the structure, obviously wouldn't work too. Descriptive statistics is the easy way, but there should be something else?
I'm not experienced in working with neural networks, but maybe there's a NN approach that could find meaningful structures in the time series data?
I'm also thinking about clustering different time series based on their similarities and check for significance, but again, is there something robust to different length/sparseness of the time series?