Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

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 …

The detection of anomalies in time series has gained ample academic and industrial
attention. However, no comprehensive benchmark exists to evaluate time-series anomaly …

In this study, we present the results of the M5 “Accuracy” competition, which was the first of
two parallel challenges in the latest M competition with the aim of advancing the theory and …

The M5 competition follows the previous four M competitions, whose purpose is to learn from
empirical evidence how to improve forecasting performance and advance the theory and …

In many supply chains, firms staged in upstream of the chain suffer from variance
amplification emanating from demand information distortion in a multi-stage supply chain …

A lot of controversy exists around the choice of the most appropriate error measure for
assessing the performance of forecasting methods. While statisticians argue for the use of …

This paper discusses how to forecast time series using generalized regression neural
networks. The main goal is to take advantage of their inherent properties to generate fast …

Forecasting future demand is of high importance for many companies as it affects
operational decisions. This is especially relevant for products with a short shelf life due to the …

This paper develops a new approach to forecast natural gas consumption via ensembles. It
combines Bootstrap Aggregation (Bagging), univariate time series forecasting methods and …