I am trying to build a dynamic pricing algorithm on intermittent data (a lot of zeros between non-zero values). I have on average 100 non-zero data points for each product. However, it seems to be quite difficult to predict the right price due to various reasons (non-regular demand, intermittent data, noises etc) so for the first step, I am trying to forecast the demand of each product using MAPA from Nikolaos Kourentzes. I hope to use this demand forecast and price-demand relation to predict the optimal price.
However, in practice these price-demand relationships tend to be non-linear so I was wondering if anyone here could guide me to papers/resources that I can follow to train non-linear model(s) on the price-demand relationship with intermittent demand data. Ideally, the trained model should be optimizable for price.
[Update 23/03/2019 : Added images/tables for elaborating Dynamic pricing]
The input is a intermittent sales data that looks something like the example shown below for most products. (Intermittent demand image courtesy to)
(Linear demand image from)
In the end, I hope to create a model that can tell me what the optimal price is given a certain demand.
(Optimal demand image from