—The main problem for analyzing time series data
with machine learning techniques such as classification and
clustering is that a high-dimensional nature of this kind of data
can cause computational difficulty in finding optimal solution.
Currently, advanced learning strategy such as deep learning
has been used extensively and effectively to improve learning
performance. In this research, we propose a method to optimize
time series analysis by adding a pre-training and fine-tuning
process of deep learning based on Deep Belief Networks and
Restricted Boltzmann Machines. On evaluating performance of
the proposed method, we use electroencephalographic,
electrocardiogram, and synthetic time series data to analyze
with classification task. The induced classification models are
assessed with the four several metrics including cluster
evaluation, purity, mean squared error, and processing time.
We comparatively compare the three learning schemes:
traditional neural networks, deep learning networks, and deep
learning networks with added a pre-training and fine-tuning
process. The results showed that all three schemes show the
same performance on predicting time series data when assessed
with mean squared error. For the processing time comparison,
neural networks technique is slightly faster than others. But
when assessed with cluster formation and purity metrics, we
found that deep learning based on the concept of Deep Belief
Networks and Restricted Boltzmann Machines that adds a
pre-training and fine-tuning process outperforms other
—Deep belief networks, deep learning, restricted
Boltzmann machines, time series analysis.
The authors are with the School of Computer Engineering, Suranaree
University of Technology (SUT), 111 University Avenue, Muang, Nakhon
Ratchasima 30000, Thailand (corresponding author: T. Thinsungnoen; Tel.:
+66819671907; e-mail: email@example.com, firstname.lastname@example.org,
Cite: Tippaya Thinsungnoen, Kittisak Kerdprasop, and Nittaya Kerdprasop, "A Deep Learning of Time Series for Efficient Analysis," International Journal of Future Computer and Communication vol. 6, no. 3, pp. 123-127, 2017.