Deep learning based forecasting methods have become the methods of choice in many
applications of time series prediction or forecasting often outperforming other approaches …

Large-scale and multidimensional spatiotemporal data sets are becoming ubiquitous in
many real-world applications such as monitoring urban traffic and air quality. Making …

Producing probabilistic forecasts for large collections of similar and/or dependent time series
is a practically highly relevant, yet challenging task. Classical time series models fail to …

The training, maintenance, deployment, monitoring, organization and documentation of
machine learning (ML) models–in short model management–is a critical task in virtually all …

Robust multivariate time series forecasting is crucial in many cyberphysical and Internet of
Things applications. Existing state-of-the-art robust forecasting models decompose time …

This work proposes a novel approach for multiple time series forecasting. At first, multi-way
delay embedding transform (MDT) is employed to represent time series as low-rank block …

We introduce Gluon Time Series (GluonTS, available at https://gluon-ts. mxnet. io), a library
for deep-learning-based time series modeling. GluonTS simplifies the development of and …

Many real-world applications show growing demand for the prediction of long sequence
time-series, such as electricity consumption planning. Long sequence time-series …

Transmission system operators (TSOs) forecast load and renewable energy generation to
maintain smooth functioning of the grid by contracting sufficient generation and reserve …

Operational forecasting often requires predicting collections of related, multivariate time
series data that are high-dimensional in nature. This can be tackled by fitting a single …