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 …

Concept drift primarily refers to an online supervised learning scenario when the relation
between the input data and the target variable changes over time. Assuming a general …

In the retail stage of a food supply chain, food waste and stock-outs occur mainly due to
inaccurate forecasting of sales which leads to incorrect ordering of products. The time series …

In most challenging data analysis applications, data evolve over time and must be analyzed
in near real time. Patterns and relations in such data often evolve over time, thus, models …

Food sales prediction is concerned with estimating future sales of companies in the food
industry, such as supermarkets, groceries, restaurants, bakeries and patisseries. Accurate …

Data-driven methods--such as machine learning and time series forecasting--are widely
used for sales forecasting in the food retail domain. However, for newly introduced products …

Real-world data usually comprise features whose interpretation depends on some
contextual information. Such contextual-sensitive features and patterns are of high interest to …

Purpose Weather affects consumer decision-making. However, academic research on how
weather factors affect specific takeaway foods is limited. This paper aims to fill in the gap and …

Data representation and similarity measurement are two basic aspects of similarity detection
in time series data mining. In this paper, we present two novel approaches to perform …

The food industry is critically depending on the accurate forecasting, wide business diversity
and cutting edge competence. A wide range of items are included in the stocks. some …