Forecasting non-negative sparse time-series data
I have two ideas here, maybe they will be helpful.

Idea 1: Model time between events

You might think of your data as being generated by two processes: the first is a distribution over time intervals, and the second is a distribution over purchase amounts. So to model your data you could create one distribution (gaussian?) over the nonzero values in your dataset, and another over the lengths of sequences of zeros (poisson?).

Idea 2: Model customer inventory

Even though the sales events in your dataset are sparse, you could spend a little time to come up with a model of why the customer is making purchases when they do. In one possible model, the customer has an inventory that shrinks over time, and they make purchases when their inventory crosses some minimum threshold. You could use your sales data to fit the slope (for linear shrinkage) or rate (for exponential shrinkage) as well as the threshold.

This could get arbitrarily complex, since the customer under this model might have different thresholds or shrink rates at different times ... but for starters it could be a useful approach to get a sense of things.