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I have a time-series dataset (daily frequency) representing the sales of a product to a customer over time. The sales is represented as the following:

$$[0, 0, 0, 0, 24, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 17, 0, 0, 0, 0, 9, 0, ...]$$

in which each number represents the sales of the product in a day.

The problem is that time-series forecast methods (ARMA, HoltWinters) work well for "continuous" and "smooth" data, but is not producing good results in this case.

I want to make a forecast of that series, with attention to 2 points: (1) assuring non-negative values and (2) sparse/ non-continuous data. Anyone knows how to approach this problem? What methods/ technique?

Thanks!

2Just to chime in, if you don't need daily resolution in your predictions, perhaps by aggregating to weekly you will have an easier time series to forecast. – Juan – 2017-07-12T16:22:36.677