I have a data-set containing 7046 unique dealer codes and their monthly sales data from April 2013-August 2018. The Financial Year for the sales data begins in the month of April for a year and ends in the month of March of the coming year. eg FY14 is April2013-March2014.
I have to forecast the sales dealer-wise. Should I go ahead with forecasting the sales for a single dealer using ARIMA and then doing the same for other dealers as well?
I tried the other queries in this platform as well as others where using multiple regression, random forest, SVM and several other techniques are used due to the presence of other predictors, but in my case, I have data in the following format:
DealerCode | Apr-13 | May-13 | June-13 |......| Aug-18 DTC06 | 16.08 | 35.285 | 85.26 |......| 81.504 DTC10 | 15.315 | 0 | 16.26 |......| 0 DTC128 | 32.535 | 0 | 34.21 |......| 0 DTC141 | 39.805 | 18.175 | 9.58 |......| 20.997
The columns where the value is 0 implies no sale has occurred in that particular month. I have thought of imputing the 0 values with median of the entire column. How should I proceed in terms of the types of techniques that can be applied? Please suggest.