Concurrent Neural Network : A model of competition between times series
This work addresses market share prediction for sales forecasting, but it is incremental as it builds on existing neural network approaches with a specific competitiveness model.
The paper tackled the problem of modeling competition between time series, such as sales cannibalization among similar products, by developing a neural network-based model with a competitiveness function based on external features like price and margin. The implementation outperformed traditional time series methods and classical neural networks on a real-world dataset for market share prediction.
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.