Evaluation of deep learning models for multi-step ahead time series prediction

• URL: http://arxiv.org/abs/2103.14250v1
• Date: Fri, 26 Mar 2021 04:07:11 GMT
• Title: Evaluation of deep learning models for multi-step ahead time series prediction
• Authors: Rohitash Chandra, Shaurya Goyal, Rishabh Gupta
• Abstract summary: 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.
• Score: 1.3764085113103222
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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