I am trying to forecast time series of product sales, I started approaching the problem by implementing the ARIMA model, I iterated over all the possibilities of the models parameters (p, d, q) and picked the one with least RMSE, problem is the forecast is not as good as I wanted it to be, so I started studying other ways of prediction, like regression.
After plotting my data in a cumulative plot, I noticed that most of the time series I had are fairly linear, so probably I can fit a linear regression model on them.
What should I use in my case, ARIMA model or linear regression, and what does ARIMA model has to offer than regression does not for it to compensate for being more complicated.
Here is a screenshot of my ARIMA forecast, and cumulative plot (weekly):
Note that 373 is the RMSE of the time series forecast, blue is prediction, red is test data
This is my data per month, the model is acting even worse in predicting the data.