Time series forecasting is fundamental for various use cases in different domains such as
energy systems and economics. Creating a forecasting model for a specific use case …

Household electricity consumption influenced by various behavioural intervention strategies
is difficult to predict due to the uncertainty that arises from human behaviours and their …

This study aims to investigate the contributions of promotional marketing activities, historical
demand and other factors to predict, and develop a big data-driven fuzzy classifier-based …

In this paper, we explored how judgment can be used to improve the selection of a
forecasting model. We compared the performance of judgmental model selection against a …

Demand forecasting works as a basis for operating, business and production planning
decisions in many supply chain contexts. Yet, how to accurately predict the manufacturer's …

The rapid rise of many e-commerce platforms for individual consumers has generated a
large amount of text-based data, and thus researchers have begun to experiment with text …

Existing sales forecasting models are not comprehensive and flexible enough to consider
dynamic changes and nonlinearities in sales time-series at the store and product levels. To …

Key to ensuring a successful tourism sector is timely policy making and detailed planning.
National policy formulation and strategic planning requires long-term forecasts at an …

Retail sectors consisting of hypermarkets, supermarkets, and convenience stores are closely
related to the economy condition of a country because they satisfy basic requirements in …

The linear regression model remains an important workhorse for data scientists. However,
many data sets contain many more predictors than observations. Besides, outliers, or …