Time Series Classification Using Images: The Case Of SAX-Like Transformation

This study concerns the classification of univariate time series. The essence of the survey is transforming time series into two-dimensional monochromatic images. Then, obtained images are classified using convolutional neural networks. Transformation of time series to images is performed in two steps. First, a time series is turned into a string of symbols from an assumed alphabet utilizing SAX-like transformation. The length of the string is supposed to be the square of a natural number. Second, the string of symbols is turned into a square matrix of size equal to the square root of the length of the string representing the time series. Then, each symbol of the matrix is turned into a square-shaped piece of pixels of a grey level determined by the symbol. So then, this operation results in an image (still of square shape) composed of squares of grey pixels. Finally, convolutional neural networks are employed to classify such images. An overall design process is presented with a focus on investigating time series-to-image two-step transformations. Experimental studies involving publicly available data sets are reported, along with an adequate comparative analyses

Time Series Classification Using Images

This work is a contribution to the field of time series classification. We propose a novel method that transforms time series into multi-channel images, which are then classified using Convolutional Neural Networks as an at-hand classifier. We present different variants of the proposed method. Time series with different characteristics are studied in this paper: univariate, multivariate, and varying lengths. Several selected methods of time-series-to-image transformation are considered, taking into account the original series values, value changes (first differentials), and changes in value changes (second differentials). In the paper, we present an empirical study demonstrating the quality of time series classification using the proposed approach