I have a conceptual question and not sure what would be the appropriate solution.
I have run a time series forecast using arima methodology. I had several years of data that I used and split my data into training and validation, and proceeded to train the model using the training portion and did a forecast on the validation data in order to evaluate the efficiency of my forecast. I have also done the preprocessing steps to clean the dataset for the outliers before I ran the model.
However, when I look at the results of the forecast in the validation set, I see that the actuals significantly exceeded the forecast for a particular date. Upon further research, I uncovered that there was a marketing campaign that was run at that time that was never before run in all the historical data for that date.
I tried different approaches using dummy variables, and somehow I am not able to figure out how I can bring my forecast to do a better job for this unseen value in the validation data that is never before seen in the training data.
Any feedback or alternative approaches to this scenario would be much appreciated. Thanks!