Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022,WIREs Data Mining and Knowledge Discovery

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Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022
WIREs Data Mining and Knowledge Discovery ( IF 6.4 ) Pub Date : 2023-09-28 , DOI: 10.1002/widm.1519
Cheng Zhang ₁ , Nilam Nur Amir Sjarif ₁ , Roslina Ibrahim ₁

Affiliation

Accurately predicting the prices of financial time series is essential and challenging for the financial sector. Owing to recent advancements in deep learning techniques, deep learning models are gradually replacing traditional statistical and machine learning models as the first choice for price forecasting tasks. This shift in model selection has led to a notable rise in research related to applying deep learning models to price forecasting, resulting in a rapid accumulation of new knowledge. Therefore, we conducted a literature review of relevant studies over the past 3 years with a view to aiding researchers and practitioners in the field. This review delves deeply into deep learning-based forecasting models, presenting information on model architectures, practical applications, and their respective advantages and disadvantages. In particular, detailed information is provided on advanced models for price forecasting, such as Transformers, generative adversarial networks (GANs), graph neural networks (GNNs), and deep quantum neural networks (DQNNs). The present contribution also includes potential directions for future research, such as examining the effectiveness of deep learning models with complex structures for price forecasting, extending from point prediction to interval prediction using deep learning models, scrutinizing the reliability and validity of decomposition ensembles, and exploring the influence of data volume on model performance.

中文翻译：

用于金融时间序列价格预测的深度学习模型：最新进展回顾：2020-2022

准确预测金融时间序列的价格对于金融部门来说至关重要且具有挑战性。由于深度学习技术的最新进展，深度学习模型正在逐渐取代传统的统计和机器学习模型，成为价格预测任务的首选。模型选择的这种转变导致将深度学习模型应用于价格预测相关的研究显着增加，从而导致新知识的快速积累。因此，我们对过去3年的相关研究进行了文献综述，以期为该领域的研究人员和实践者提供帮助。这篇综述深入研究了基于深度学习的预测模型，提供了有关模型架构、实际应用及其各自优缺点的信息。尤其，提供了有关价格预测的高级模型的详细信息，例如 Transformer、生成对抗网络 (GAN)、图神经网络 (GNN) 和深度量子神经网络 (DQNN)。目前的贡献还包括未来研究的潜在方向，例如检查具有复杂结构的深度学习模型在价格预测中的有效性，使用深度学习模型从点预测扩展到区间预测，审查分解集成的可靠性和有效性，以及探索数据量对模型性能的影响。

更新日期：2023-09-28

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