In recent years, deep learning techniques have outperformed traditional models in many machine learning tasks. Deep neural networks have successfully been applied to address time series forecasting problems, which is a very important topic in data mining. They have proved to be an effective solution given their capacity to automatically learn the temporal dependencies present in time series. However, selecting the most convenient type of deep neural network and its parametrization is a complex task that requires considerable expertise. Therefore, there is a need for deeper studies on the suitability of all existing architectures for different forecasting tasks. In this work, we face two main challenges: a comprehensive review of the latest works using deep learning for time series forecasting and an experimental study comparing the performance of the most popular architectures. The comparison involves a thorough analysis of seven types of deep learning models in terms of accuracy and efficiency. We evaluate the rankings and distribution of results obtained with the proposed models under many different architecture configurations and training hyperparameters. The datasets used comprise more than 50,000 time series divided into 12 different forecasting problems. By training more than 38,000 models on these data, we provide the most extensive deep learning study for time series forecasting. Among all studied models, the results show that long short-term memory (LSTM) and convolutional networks (CNN) are the best alternatives, with LSTMs obtaining the most accurate forecasts. CNNs achieve comparable performance with less variability of results under different parameter configurations, while also being more efficient.

中文翻译：

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用于时间序列预测的深度学习架构的实验回顾

近年来，深度学习技术在许多机器学习任务中的表现都优于传统模型。深度神经网络已成功应用于解决时间序列预测问题，这是数据挖掘中非常重要的课题。鉴于它们能够自动学习时间序列中存在的时间依赖性，它们已被证明是一种有效的解决方案。然而，选择最方便的深度神经网络类型及其参数化是一项复杂的任务，需要相当多的专业知识。因此，需要对所有现有架构对不同预测任务的适用性进行更深入的研究。在这项工作中，我们面临两个主要挑战：全面回顾使用深度学习进行时间序列预测的最新作品，以及比较最流行架构性能的实验研究。比较涉及对七种深度学习模型在准确性和效率方面的全面分析。我们评估了在许多不同的架构配置和训练超参数下使用所提出的模型获得的结果的排名和分布。使用的数据集包含超过 50,000 个时间序列，分为 12 个不同的预测问题。通过在这些数据上训练超过 38,000 个模型，我们为时间序列预测提供了最广泛的深度学习研究。在所有研究的模型中，结果表明长短期记忆（LSTM）和卷积网络（CNN）是最好的选择，使用 LSTM 获得最准确的预测。CNN 在不同的参数配置下实现了可比的性能，结果的可变性更小，同时也更有效。