Time series prediction with neural networks has been the 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. The deep learning methods comprise simple recurrent neural networks, long short-term memory (LSTM) networks, bidirectional LSTM networks, encoder-decoder LSTM networks, and convolutional neural networks. We provide a further comparison with simple neural networks that use stochastic gradient descent and adaptive moment estimation (Adam) for training. We focus on univariate time series for multi-step-ahead prediction from benchmark time-series datasets and provide a further comparison of the results with related methods from the literature. The results show that the bidirectional and encoder-decoder LSTM network provides the best performance in accuracy for the given time series problems.
翻译:在过去几十年中,通过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,比较了前方多步时间序列预测的深层次学习模型的性能。深层学习方法包括简单的经常性神经网络、长期短期内存(LSTM)网络、双向LSTM网络、编码器-脱coder LSTM网络和共生神经网络。我们进一步比较了使用随机梯度梯度梯度和适应时间估计(Adam)进行培训的简单神经网络。我们侧重于从基准时间序列数据集中进行多步前预测的单向时间序列时间序列,并进一步比较文献相关方法的结果。结果显示,双向和编码解码LSTM网络为特定时间序列问题提供了最佳的准确性表现。