In recent years, deep learning techniques have outperformed traditional models in many
machine learning tasks. Deep neural networks have successfully been applied to address …

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

Although we have witnessed great success of pre-trained models in natural language
processing (NLP) and computer vision (CV), limited progress has been made for general …

Time series forecasting holds significant importance in many real-world dynamic systems
and has been extensively studied. Unlike natural language process (NLP) and computer …

Time series forecasting is an important problem across many domains, including predictions
of solar plant energy output, electricity consumption, and traffic jam situation. In this paper …

Stock market prediction has been a classical yet challenging problem, with the attention from
both economists and computer scientists. With the purpose of building an effective prediction …

The M4 Competition follows on from the three previous M competitions, the purpose of which
was to learn from empirical evidence both how to improve the forecasting accuracy and how …

We focus on solving the univariate times series point forecasting problem using deep
learning. We propose a deep neural architecture based on backward and forward residual …

This paper presents the winning submission of the M4 forecasting competition. The
submission utilizes a dynamic computational graph neural network system that enables a …

In the last few years, machine learning (ML) and artificial intelligence have seen a new wave
of publicity fueled by the huge and ever‐increasing amount of data and computational …