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 …

We extend neural basis expansion analysis (NBEATS) to incorporate exogenous factors.
The resulting method, called NBEATSx, improves on a well-performing deep learning …

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 …

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 …

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 …

This paper tackles the modelling of large, complex and multivariate time series panels in a
probabilistic setting. To this extent, we present a novel approach reconciling classical state …

The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is
motivating the search for high-performance and efficient spiking neural networks to run on …