Time series prediction with neural networks have been focus of much research in the past few decades. Given the recent deep learning revolution, there has been much attention in using deep learning models for time series prediction, and hence it is important to evaluate their strengths and weaknesses. In this paper, we present an evaluation study that compares the performance of deep learning models for multi-step ahead time series prediction. Our deep learning methods compromise of simple recurrent neural networks, long short term memory (LSTM) networks, bidirectional LSTM, encoder-decoder LSTM networks, and convolutional neural networks. We also provide comparison with simple neural networks use stochastic gradient descent and adaptive gradient method (Adam) for training. We focus on univariate and multi-step-ahead prediction from benchmark time series datasets and compare with results from from the literature. The results show that bidirectional and encoder-decoder LSTM provide the best performance in accuracy for the given time series problems with different properties.
翻译:在过去几十年中,与神经网络进行的时间序列预测一直是许多研究的重点。鉴于最近的深层次学习革命,在使用深层次学习模型进行时间序列预测方面,人们非常关注如何使用深层次学习模型进行时间序列预测,因此重要的是要评价这些模型的优缺点。在本文件中,我们提出了一项评价研究,比较了用于多阶段提前时间序列预测的深层次学习模型的性能。我们深层次学习的简单经常性神经网络、长期内存(LSTM)网络、双向LSTM(LSTM)网络、编码脱钩LSTM网络和连动神经网络。我们还与简单的神经网络进行比较,使用随机梯度梯度梯度和适应梯度方法(Adam)进行培训。我们侧重于从基准时间序列数据集中进行单向和多步头预测,并与文献结果进行比较。结果显示,双向和编码脱钩LSTM(LSTM)网络提供了不同属性的时间序列问题的最佳准确性能。