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 …

Abstract Recurrent Neural Networks (RNNs) have become competitive forecasting methods,
as most notably shown in the winning method of the recent M4 competition. However …

The smart grid concept is introduced to accelerate the operational efficiency and enhance
the reliability and sustainability of power supply by operating in self-control mode to find and …

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 …

Anomaly detection for time-series data has been an important research field for a long time.
Seminal work on anomaly detection methods has been focussing on statistical approaches …

We review the results of six forecasting competitions based on the online data science
platform Kaggle, which have been largely overlooked by the forecasting community. In …

Recent trends in the Machine Learning (ML) and in particular Deep Learning (DL) domains
have demonstrated that with the availability of massive amounts of time series, ML and DL …

The prevalence of approaches based on gradient boosted trees among the top contestants
in the M5 competition is potentially the most eye-catching result. Tree-based methods out …

Many businesses and industries nowadays rely on large quantities of time series data
making time series forecasting an important research area. Global forecasting models that …