I'm trying to generate a one-year forecast for the number of units sold by a retail company. I'm using monthly data from 2017 and 2018. The forecast is for 2019, and I'm using the data from the months that have already passed (January-July) as test data. So far, I have had good results generating the forecast for every category using an innovations state space model for exponential smoothing as described by Hyndman et al. (2018)
The problem is that I just came across a category (let's call it category X) where the number of units sold had a significant overall drop since January due to a change of suppliers. I believe this drop will continue during the rest of the year due to business reasons. Data looks like this.
Is there a way I can incorporate this information into the innovations state space model? Should I use a different method to forecast category X? Any suggestion is welcomed.
Hyndman, R.J., Athanasopoulos, G. (2018) Forecasting: principles and practice, 2nd edition, OTexts: Melbourne, Australia. OTexts.com/fpp2. Accessed on August 2019.