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

This paper reviews the research literature on forecasting retail demand. We begin by
introducing the forecasting problems that retailers face, from the strategic to the operational …

Many real-world applications require the prediction of long sequence time-series, such as
electricity consumption planning. Long sequence time-series forecasting (LSTF) demands a …

We present a novel approach to probabilistic time series forecasting that combines state
space models with deep learning. By parametrizing a per-time-series linear state space …

Forecasting high-dimensional time series plays a crucial role in many applications such as
demand forecasting and financial predictions. Modern datasets can have millions of …

Probabilistic forecasting, ie, estimating a time series' future probability distribution given its
past, is a key enabler for optimizing business processes. In retail businesses, for example …

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 …

Classifying forecasting methods as being either of a “machine learning” or “statistical” nature
has become commonplace in parts of the forecasting literature and community, as …

This paper takes a step towards temporal reasoning in a dynamically changing video, not in
the pixel space that constitutes its frames, but in a latent space that describes the non-linear …

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 …