My dataset consists of transactions at the daily level; they can be negative or positive, for a total of one year of data.
I want to fit a model that predicts daily transactions, spendings or income, lets say for one extra month.
My dependent variable will be transaction amount; my features will be a set of variables, in this example, just day and type of transaction.
I want to fit a naive linear regression.
Now let's imagine my data looked like:
a. I can see a clear (positive) outlier in the data. My regression will be extremely sensitive to that data point. If the amount variable would have been just positive, I would have taken the log. What to do here instead?
b. Is there any approach other than regression that could relax for the presence of the outliers? Given the obvious seasonality of the data, a time series approach would be more appropriate? Any hint?