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Sci-Hub | Deep Learning for Time Series Forecasting: A Survey | 10.1089/big.2020.0159
Source: sci-hub.et-fine.com
Article summary:
1. This article presents a survey of deep learning techniques for time series forecasting.
2. It discusses the advantages and limitations of different approaches, such as recurrent neural networks, convolutional neural networks, and long short-term memory networks.
3. The authors provide an overview of the current state of research in this field and suggest potential areas for future work.
Article analysis:
The article is written by experienced researchers in the field of deep learning for time series forecasting, which lends it credibility. The authors provide a comprehensive overview of the current state of research in this field, including both advantages and limitations of different approaches. They also provide suggestions for potential areas for future work.
The article does not appear to be biased or one-sided; it provides an objective overview of the current state of research in this field without promoting any particular approach or technique. All claims are supported with evidence from existing literature, and counterarguments are explored where appropriate. There is no promotional content or partiality present in the article. Possible risks associated with deep learning techniques are noted throughout the text, providing readers with a balanced view on the topic. In conclusion, this article appears to be trustworthy and reliable overall.
Topics for further research:
Time series forecasting using deep learning
Deep learning for time series analysis
Advantages and limitations of deep learning for time series forecasting
Recent advances in deep learning for time series forecasting
Challenges in deep learning for time series forecasting
Applications of deep learning for time series forecasting