EDIT: I just want to note that both David and Richard's answers (and comments) I think are needed to paint the full picture in response. To me I still haven't felt a real need to try out time-series frameworks from the given replies if all I can about is strong predictions and have a good sense of how to set up the regression formula, but I do take the points taken on interpretation of the coefficients and overall efficiency (and that, perhaps, I'm doing it the "hard" way).
I'm a bit naive to time-series related models like ARIMA as I can't seem to find a justification for them compared to a well-setup regression model for forecasting. Numerous responses online point to the vulnerability of linear regression due to thinks like autocorrelated errors, seasonality, and extrapolation, but it seems to me I can accommodate much of this with good data prep:
- Seasonality - Model it. If there are periodic dips, I've had good luck catching them with various flag columns (month, quarter, even years, etc).
- Autocorrelation - Include lagged values. Taking deltas, rolling means, and various statistics against prior values seems to help nicely.
- Extrapolation - I don't see how time-series approaches navigate this any better since rare to no series is truly stationary into the future. I've found that keeping a running count from an initial reference point (i.e. months since start) seems to help general directional trends, and at some point you need to retrain anyways.
Adding on to that, regression models allow for a standard means of including multiple other input features (for instance market data if predicting sales) that do help that extrapolation quandary, as well as the benefits of a typical "optimize it to heck" framework of machine learning... I've never found the justification for a true time-series route.
Can someone help explain what I'm missing in practical use case terms? Or is it perhaps just that regression models do require that careful data modelling before use?