This paper reviews the research literature on forecasting retail demand. We begin by
introducing the forecasting problems that retailers face, from the strategic to the operational …

In recent two decades, artificial neural networks have been extensively used in many
business applications. Despite the growing number of research papers, only few studies …

There has been a growing interest in the field of neural networks for prediction in recent
years. In this research, a public dataset including the sales history of a retail store is …

Predicting air quality is a complex task due to the dynamic nature, volatility, and high
variability in time and space of pollutants and particulates. At the same time, being able to …

The application of predictive analytics (PA) in Supply Chain Management (SCM) has
received growing attention over the last years, especially in demand forecasting. The …

Demand forecasting is an important task for retailers as it is required for various operational
decisions. One key challenge is to forecast demand on special days that are subject to vastly …

The demand is often forecasted using econometric (regression) or statistical forecasting
methods. However, most of these methods lack the ability to model both temporal (linear and …

In this work we present a large scale comparison study for the major machine learning
models for time series forecasting. Specifically, we apply the models on the monthly M3 time …

This paper proposes a novel forecasting method that combines the deep learning method–
long short-term memory (LSTM) networks and random forest (RF). The proposed method …

The demand for a particular product or service is typically associated with different
uncertainties that can make them volatile and challenging to predict. Demand …