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 50000 time series divided into 12 different forecasting problems. By training more than 38000 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.
翻译:近年来,深层学习技术在许多机器学习任务中优于传统模式;深神经网络成功地用于解决时间序列预测问题,这是数据挖掘中一个非常重要的专题;事实证明,深神经网络是一个有效的解决办法,因为其有能力自动学习时间序列中存在的时间依赖性;然而,选择最方便的深神经网络类型及其平衡是一个复杂的任务,需要相当多的专门知识;因此,需要更深入地研究所有现有结构是否适合不同预测任务。在这项工作中,我们面临两个主要挑战:利用对时间序列预测的深入学习,全面审查最新工程;以及比较最受欢迎的结构的绩效的实验性研究。这种比较涉及从准确性和效率方面对七类深度学习模型进行透彻分析。我们评估在许多不同的结构配置和培训超光谱下与拟议模型所获结果的排名和分布情况。所使用的数据集包括5万多个时间序列,分为12个不同的预测问题。在这些数据上培训了38,000多个模型,我们提供了最广泛的CN短期实时序列,同时进行了最广泛的学习性能 - ST-时间序列预测。所有研究都用最不甚易变的模型来进行最佳的学习结果。