I am interested in getting a better sense as to when to use time series techniques.
Let's say you have a data set with units sold as the response. Your goal is to predict units sold on any given day. If you were to plot units sold against time, let's say you see an obvious increasing trend and seasonality.
When would it be better to use a time series technique vs. just building a regression tree and hope it splits on features like (day count, month, year, day, etc...)?
On the one hand, it makes sense that tomorrow's sales will be closer to today's sales than it would be to sales 10 year ago. This would suggest a time series model.
But on the other hand, realistically, there is little economic rationale stating that tomorrow's sale's depends on today's sales. Wouldn't using a tree be more useful as basically you are dividing time into a lot of thresholds. For example, if we are in 2020 as opposed to 2010, predict a higher average sale. If we are in summer as opposed to winter, predict a higher average sale. To capture the overall increasing trend, we can include day count as a feature, and thus higher day counts lead to higher sales.
So when we want to use a time series model?