Time Series Classification with Discrete Wavelet Transformed Data: Insights from an Empirical Study

Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification ac- curacy, and that residual details left by popular wavelet compression techniques can sometimes even help achieve higher classification accuracy than the raw time series data, as they better capture essential local features

Extracting Statistical Graph Features for Accurate and Efficient Time Series Classification

This paper presents a multiscale visibility graph representation for time series as well as feature extraction methods for time series classification (TSC). Unlike traditional TSC approaches that seek to find global similarities in time series databases (eg., Nearest Neighbor with Dynamic Time Warping distance) or methods specializing in locating local patterns/subsequences (eg., shapelets), we extract solely statistical features from graphs that are generated from time series. Specifically, we augment time series by means of their multiscale approximations, which are further transformed into a set of visibility graphs. After extracting probability distributions of small motifs, density, assortativity, etc., these features are used for building highly accurate classification models using generic classifiers (eg., Support Vector Machine and eXtreme Gradient Boosting). Thanks to the way how we transform time series into graphs and extract features from them, we are able to capture both global and local features from time series. Based on extensive experiments on a large number of open datasets and comparison with five state-of-the-art TSC algorithms, our approach is shown to be both accurate and efficient: it is more accurate than Learning Shapelets and at the same time faster than Fast Shapelets

DSCo-NG: A Practical Language Modeling Approach for Time Series Classification

The abundance of time series data in various domains and their high dimensionality characteristic are challenging for harvesting useful information from them. To tackle storage and processing challenges, compression-based techniques have been proposed. Our previous work, Domain Series Corpus (DSCo), compresses time series into symbolic strings and takes advantage of language modeling techniques to extract from the training set knowledge about different classes. However, this approach was flawed in practice due to its excessive memory usage and the need for a priori knowledge about the dataset. In this paper we propose DSCo-NG, which reduces DSCo’s complexity and offers an efficient (linear time complexity and low memory footprint), accurate (performance comparable to approaches working on uncompressed data) and generic (so that it can be applied to various domains) approach for time series classification. Our confidence is backed with extensive experimental evaluation against publicly accessible datasets, which also offers insights on when DSCo-NG can be a better choice than others

Deep Time-Series Clustering: A Review

We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives

Time Series Classification with Discrete Wavelet Transformed Data

peer reviewe

Time Series Classification with Discrete Wavelet Transformed Data: Insights from an Empirical Study

Abstract Time series mining has become essential for extracting knowledge from the abundant data that flows out from many application domains. To overcome storage and processing challenges in time series mining, compression techniques are being used. In this paper, we investigate the loss/gain of performance of time series classification approaches when fed with lossy-compressed data. This empirical study is essential for reassuring practitioners, but also for providing more insights on how compression techniques can even be effective in reducing noise in time series data. From a knowledge engineering perspective, we show that time series may be compressed by 90% using discrete wavelet transforms and still achieve remarkable classification accuracy, and that residual details left by popular wavelet compression techniques can sometimes even help achieve higher classification accuracy than the raw time series data, as they better capture essential local features