Time Series Generation - Multi Dimensional Time Series Data
Time series data must contain all your observations with some standard effect of time (Bit obvious here). If i wants to test theory on some model &amp; need some dataset then the parameters would be like, Timestamps * no. of features (includes player's position with respect to a source). A 2d tensor would suffice. My reason for not making a 3d tensor is that it would lead to more complex scenario where i would have to co-relate the 3rd dimension (No of players [N]) with first 2 dimensions to predict. Better to simplify the positions &amp; in a single row i can have multiple labels as every position will matter to my model.

Time series generation generally falls under continuous prediction with previous data predicted taken to be an observation. I would rather make it fall under Reinforcement learning. Yes you can work with supervised learning in mind but try RL approach as well.

As per the missing values i would say that remove them if they don't make more than 10-15% of data. There are no fix bars on the mentioned percentage. If it is more than that please fill in with interpolation or rolling average (both benefited me). Rest pre-processing depends upon type of data, normalise data, remove outlier etc.

Yes it will affect the data, but you can generate data using sign wave's different fluctuation for your testing or use any other functions to generate a signal(scipy preferred).

Currently i am testing bidirectional lstm - CNN combination for my time series, yes ARIMA is good but doing a little of this expirmenet won't hurt. I would say go for CNN-Any of RNN combination for time series.

Hope this helps.