  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.
