The development of deep learning technology has brought great improvements to the field
of time series forecasting. Short sequence time-series forecasting no longer satisfies the …

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 …

Transformers have achieved superior performances in many tasks in natural language
processing and computer vision, which also triggered great interest in the time series …

Recent progress in neural forecasting accelerated improvements in the performance of large-
scale forecasting systems. Yet, long-horizon forecasting remains a very difficult task. Two …

Self-supervised learning (SSL) has recently achieved impressive performance on various
time series tasks. The most prominent advantage of SSL is that it reduces the dependence …

Generative modeling of multivariate time series has remained challenging partly due to the
complex, non-deterministic dynamics across long-distance timesteps. In this paper, we …

This paper addresses the mid-term electricity load forecasting problem. Solving this problem
is necessary for power system operation and planning as well as for negotiating forward …

Over the past years, foundation models have caused a paradigm shift in machine learning
due to their unprecedented capabilities for zero-shot and few-shot generalization. However …

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 …

We introduce the Gluon Time Series Toolkit (GluonTS), a Python library for deep learning
based time series modeling for ubiquitous tasks, such as forecasting and anomaly detection …