Deep Learning for Time Series Forecasting: Advances and Open Problems

IF 2.4 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Angelo Casolaro, Vincenzo Capone, Gennaro Iannuzzo, Francesco Camastra
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引用次数: 0

Abstract

A time series is a sequence of time-ordered data, and it is generally used to describe how a phenomenon evolves over time. Time series forecasting, estimating future values of time series, allows the implementation of decision-making strategies. Deep learning, the currently leading field of machine learning, applied to time series forecasting can cope with complex and high-dimensional time series that cannot be usually handled by other machine learning techniques. The aim of the work is to provide a review of state-of-the-art deep learning architectures for time series forecasting, underline recent advances and open problems, and also pay attention to benchmark data sets. Moreover, the work presents a clear distinction between deep learning architectures that are suitable for short-term and long-term forecasting. With respect to existing literature, the major advantage of the work consists in describing the most recent architectures for time series forecasting, such as Graph Neural Networks, Deep Gaussian Processes, Generative Adversarial Networks, Diffusion Models, and Transformers.
时间序列预测的深度学习:进展和开放问题
时间序列是按时间顺序排列的数据序列,通常用于描述一种现象如何随时间演变。时间序列预测,估计时间序列的未来值,允许决策策略的实施。深度学习是目前机器学习的前沿领域,应用于时间序列预测可以处理其他机器学习技术通常无法处理的复杂和高维时间序列。这项工作的目的是为时间序列预测提供最先进的深度学习架构的回顾,强调最近的进展和开放的问题,并关注基准数据集。此外,该工作还明确区分了适合短期和长期预测的深度学习架构。就现有文献而言,这项工作的主要优势在于描述了时间序列预测的最新架构,如图神经网络、深度高斯过程、生成对抗网络、扩散模型和变形器。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Information (Switzerland)
Information (Switzerland) Computer Science-Information Systems
CiteScore
6.90
自引率
0.00%
发文量
515
审稿时长
11 weeks
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