Periodic Time Series Data Analysis by Deep Learning Methodology

The detection of periodicity in a time series is considered a challenge in many research areas. The difficulty of period length extraction involves the varying noise levels among working environments. A system that performs well in one environment may not be accurate in another. Different methods, including deep neural networks, have been proposed across many applications to find suitable solutions to the period length extraction problem. This article proposes a convolutional neural network (CNN) based period classification algorithm, named PCA, to detect the dataset periods. In particular, assuming that a data stream contains periodical features, the PCA utilizes historical labeled data as training material and classifies new instances accordingly based on their periods. Its performance has been tested on both synthetic and real-world periodic time series data (PTSD) with very encouraging results. In particular, We have observed that the PCA is capable of achieving 100% accuracy in the case of low noise PTSD. Even the training of the PCA is not economical if the data do not contain much noise, it still demonstrates high performance on both synthetic and real-world datasets. Besides, we have shown that our new algorithm can capture the relationship between the shape of the waves and the target period, which is significantly different from the classical methods that mainly focus on the wave's amplitude.