Extracting Texture Features for Time Series Classification

Abstract

Abstract-Time series are present in many pattern recognition applications related to medicine, biology, astronomy, economy, and others. In particular, the classification task has attracted much attention from a large number of researchers. In such a task, empirical researches has shown that the 1-Nearest Neighbor rule with a distance measure in time domain usually performs well in a variety of application domains. However, certain time series features are not evident in time domain. A classical example is the classification of sound, in which representative features are usually present in the frequency domain. For these applications, an alternative representation is necessary. In this work we investigate the use of recurrence plots as data representation for time series classification. This representation has well-defined visual texture patterns and their graphical nature exposes hidden patterns and structural changes in data. Therefore, we propose a method capable of extracting texture features from this graphical representation, and use those features to classify time series data. We use traditional methods such as Grey Level Co-occurrence Matrix and Local Binary Patterns, which have shown good results in texture classification. In a comprehensible experimental evaluation, we show that our method outperforms the state-ofthe-art methods for time series classification.

Lagged encoding for image‐based time series classification using convolutional neural networks

“…de Souza et al , Yu et al , Penatti and Santos , and Nima Hatami encoded time series as Recurrence Plots. Next, images are classified using various Neural Networks.…”

Large-Scale Similarity-Based Time Series Mining

“…This work resulted in two publications, namely one in conference [Yeh et al 2016] and the other in journal [Yeh et al 2017], both indexed as A1. Moreover, the candidate collaborated in efforts for time series classification [Souza et al 2014, Giusti et al 2015, Giusti et al 2016 (conferences indexed as A2, not indexed -h-index: 12 -, B1, and not indexed -h-index: 25-, respectively). The latter has an extension submitted to the DAMI journal (Qualis A1) 3 .…”

Signal classification by similarity and feature extraction with application in automatic insect identification

“…Our proposal has been successfully used in time series classification [Silva et al 2013b] and in music information retrieval [Silva et al 2013a]. The proposal of using unthresholded recurrence plots for classification of time series, instead of extracting features of the binary recurrence matrix, opened a new path for other methods such as the use of image texture descriptors [Souza et al 2014].…”