MACD-Histogram-based Fully Convolutional Neural Networks for Classifying Time Series

With the widespread use of sensor devices, time series data have become ubiquitous. Therefore, many academic researchers and industrial practitioners have been conducting the development of analysis methods for time series. In particular, time series classification is one of the common tasks for time series analysis. Time series classification predicts the class label of a time series not having its class label. In the era of Internet of things (IoT), developing new models for classifying time series is a well-known grand challenge because time series classification is the most difficult problem in time series analysis and it covers many different application domains in IoT. This paper focuses on time series classifiers that employ deep learning techniques for univariate time series classification. Fully convolutional neural network (FCN) and hybrid models based on FCN are the most successful deep neural networks. In this paper, a new FCN-based model using the moving average convergence divergence (MACD) histogram is proposed. The MACD histogram can capture local features of the time series. In this study, our new model uses the MACD histogram, where the input of our proposed model is the MACD histogram. Moreover, to enhance the representation of an input layer representation, multi-channel input for the FCN model is proposed. In the multi-channel input, both values of a time series and the MACD histogram of the time series are input to the FCN model. Experiments were conducted using an actual time series benchmark datasets, which is the UCR Time Series Classification Archive with 85 different types of time series datasets. The experimental results show that the classification performance of the proposed model outperforms not only FCN but also FCN-LSTM.