I have a dataset of a couple of EV charging stations (10 min frequency) over 1 year. This data consists of lots of 0s, since there is no continuous flow of cars coming to charge, but rather reoccurring charging events as peaks (for example from 7-9am seems to be a frequent charging timeframe when people are coming to the office). I have also aggregated weather and weekday/holiday data to be used as features.

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I now wish to predict the energy demand for a timeframe of 6h in the future. So far I have tried SARIMA with terrible results, since the algorithm seems to be obscured by the sparse data.

I have tried different transformations (Box Cox, Normalization, Standardization), differencing, auto-arima for optimal parameters, so far no luck.

I am willing to try different machine learning as well as statistical algorithms. Does anyone have some recommendations as of what I can do to generate a moderatly accurate prediction with a sparse dataset?


1 Answer 1


One option is to model the problem as a Poisson distribution . λ, the event rate, is the only parameter to estimate. Then the number of events timeframe can be estimated.


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