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 …

Forecast combinations have flourished remarkably in the forecasting community and, in
recent years, have become part of mainstream forecasting research and activities …

What will be the global impact of the novel coronavirus (COVID-19)? Answering this
question requires accurate forecasting the spread of confirmed cases as well as analysis of …

Recent studies have shown that deep learning models such as RNNs and Transformers
have brought significant performance gains for long-term forecasting of time series because …

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 …

One unique property of time series is that the temporal relations are largely preserved after
downsampling into two sub-sequences. By taking advantage of this property, we propose a …

Abstract A BCMO-ANN algorithm for vibration and buckling optimization of functionally
graded porous (FGP) microplates is proposed in this paper. The theory is based on a unified …

Anomaly detection for time-series data has been an important research field for a long time.
Seminal work on anomaly detection methods has been focussing on statistical approaches …

The M5 competition follows the previous four M competitions, whose purpose is to learn from
empirical evidence how to improve forecasting performance and advance the theory and …