Binary classification model with time series as variables 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?
I think one of your statements is simply a misunderstanding of the relationship of data structure and analysis. When you say "Melting the data, due to the structure, obviously wouldn't work too." you seem to failing to understand that melting the data properly would require construction of a supplemental covariate that would encode the "column location" of the values. In longitudinal data this location (in the "wide" version of the data) would become the "time" variable in the "long" format. Regression and neural network methods can handle such a format.

I intended that my comment only be a suggestion for improving a search strategy,  since you had not described the task or the inputs in any great detail. So I'm "answering" with some links resulting from several variations on a search strategy along the lines of ("binary outcome" OR "signal detection" OR classification) AND ("logistic regression" OR "neural networks") AND longitudinal AND missing:

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5401893/

http://www.stats.ox.ac.uk/~snijders/WrightLondon2009.pdf

http://www.drizopoulos.com/pdf/Slides/jsm_2015.pdf

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3834930/

https://dl.acm.org/citation.cfm?id=3167596

https://mlhc17mit.github.io/slides/lecture7.pdf

https://www.cs.cmu.edu/~epxing/Class/10708-17/project-reports/project8.pdf

https://arxiv.org/pdf/1802.04664.pdf