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I have a continuous variable, sampled over a period of a year at irregular intervals. Some days have more than one observation per hour, while other periods have nothing for days. This makes it particularly difficult to detect patterns in the time series, because some months (for instance October) are highly sampled, while others are not.

My question is what would be the best approach to model this time series?

- I believe most time series analysis techniques (like ARMA) need a fixed frequency. I could aggregate the data, in order to have a constant sample or choose a sub-set of the data that is very detailed. With both options I would be missing some information from the original dataset, that could unveil distinct patterns.
- Instead of decomposing the series in cycles, I could feed the model with the entire dataset and expect it to pick up the patterns. For instance, I transformed the hour, weekday and month in categorical variables and tried a multiple regression with good results (R2=0.71)

I have the idea that machine learning techniques such as ANN can also pick these patterns from uneven time series, but I was wondering if anybody has tried that, and could provide me some advice about the best way of representing time patterns in a Neural network.

Thank you for providing an answer to the question. Any chance you could make your answer more explicit so that it directly answers the question? – shepan6 – 2020-07-11T18:41:19.197