Long-term Demand Forecasting: using an Ensemble of Neural Networks to Improve Accuracy

Abstract

This research paper proposes a method of long-term demand forecasting based on an ensemble of neural networks that considers the novelty of the data. A tool for creating the ensemble was developed that uses a bagging technique as well as a modification that allows for the relevance and novelty of the data to be considered when creating training samples for each model in the ensemble. The study examines and compares the developed method with known approaches to long-term demand forecasting. Experimental results have indicated that the proposed approach allows for obtaining more accurate and reliable demand forecasts compared to existing methods. The results emphasize the importance of data in the demand forecasting process and indicate the potential of the proposed method to eventually improve inventory management strategies and product planning.