I'm working on a time series model which predicts daily sales. Below is a simplified depiction of the time series I'm trying to model.
It is a stationary series and it takes certain 'shock'(drastic increase in daily sales) and the effect of this 'shock' fades away gradually(almost reciprocally). My model was basically based on seasonal trend decomposition by Loess, but since this 'shock' happens not in a periodic way, the seasonality component couldn't capture the effect of it. I'm trying to add a new regressor to take these effects into account. Are there any good solution to encode this variable? I can identify the initial dates when these effects start, but the way they fade away has to be modeled, I think. Sorry about not including the actual data or model summary, etc. It is corporate data, so it's proprietary.
Thanks in advance.