Jun 1, 2024 · This work surveys the literature on deep learning techniques for time series forecasting. The focus hereby lies on the characteristics and reasons for using ...

Dec 18, 2020 · The most common deep learning architectures that are currently being successfully applied to predict time series are described, highlighting their advantages ...

... survey the breadth of the recent deep forecasting literature. Research areas. Machine learning. Tags. Time series · Demand forecasting · Deep learning. Journal.

Mar 3, 2023 · Our survey paves the way towards developing a single widely used and accepted benchmark dataset for time series data, built on the various frequently used ...

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

Deep Learning has been successfully applied to many application domains, yet its advantages have been slow to emerge for time series forecasting.

Deep learning, one of the most remarkable techniques of machine learning, has been a major success in many fields, including image processing, speech.

This article surveys common encoder and decoder designs used in both one-step-ahead and multi-horizon time-series forecasting.

We also empirically compare different data augmentation methods for different tasks including time series classification, anomaly detection, and forecasting.

Dec 3, 2020 · Time series forecasting has become a very intensive field of research, which is even increasing in recent years. Deep neural networks have ...