Concurrent Neural Network : A model of competition between times series

TL;DR

This paper introduces a neural network model that captures competition between time series, such as sales of similar products, by modeling cannibalization effects with a competitiveness function influenced by external features.

Contribution

It proposes a novel neural network approach to model competition and provides theoretical error guarantees under certain conditions.

Findings

Outperforms traditional time series methods in market share prediction

Provides theoretical error bounds for the model

Effective in real-world sales prediction scenarios

Abstract

Competition between times series often arises in sales prediction, when similar products are on sale on a marketplace. This article provides a model of the presence of cannibalization between times series. This model creates a "competitiveness" function that depends on external features such as price and margin. It also provides a theoretical guaranty on the error of the model under some reasonable conditions, and implement this model using a neural network to compute this competitiveness function. This implementation outperforms other traditional time series methods and classical neural networks for market share prediction on a real-world data set.