Time Series Data Mining Tasks Research Articles

Deep learning framework for time series classification based on multiple imaging and hybrid quantum neural networks

Time series classification (TSC) has attracted a lot of attention for time series data mining tasks and has been applied in various fields. With the success of deep learning (DL) in computer vision recognition, people are starting to use deep learning to tackle TSC tasks. Quantum neural networks (QNN) have recently demonstrated their superiority over traditional machine learning in methods such as image processing and natural language processing, but research using quantum neural networks to handle TSC tasks has not received enough attention. Therefore, we proposed a learning framework based on multiple imaging and hybrid QNN (MIHQNN) for TSC tasks. We investigate the possibility of converting 1D time series to 2D images and classifying the converted images using hybrid QNN. We explored the differences between MIHQNN based on single time series imaging and MIHQNN based on the fusion of multiple time series imaging. Four quantum circuits were also selected and designed to study the impact of quantum circuits on TSC tasks. We tested our method on several standard datasets and achieved significant results compared to several current TSC methods, demonstrating the effectiveness of MIHQNN. This research highlights the potential of applying quantum computing to TSC and provides the theoretical and experimental background for future research.

Time Series Data Mining for Sport Data: a Review

Abstract Time series data mining deals with extracting useful and meaningful information from time series data. Recently, the increasing use of temporal data, in particular time series data, has received much attention in the literature. Since most of sports data contain time information, it is natural to consider the temporal dimension in form of time series. However, in sports, the effective use of time series data mining techniques is still under development. The main goal of this paper is therefore to serve as an introduction to time series data mining and a glossary for interested researchers from the sports community. The paper gives an overview about current data mining tasks and tries to identify their potential research direction for further investigation. Furthermore, we want to draw more attention with respect to the importance of mining approaches with sport data and their particular challenges beyond usual time series data mining tasks.

Cross-recurrence analysis for pattern matching of multidimensional physiological signals.

Cross-recurrence quantification analysis (CRQA), based on the cross-recurrence plot (CRP), is an effective method to characterize and quantify the nonlinear interrelationships between a pair of nonlinear time series. It allows the flexibility of reconstructing signals in the phase space and to identify different types of patterns at arbitrary positions between trajectories. These advantages make CRQA attractive for time series data mining tasks, which have been of recent interest in the literature. However, little has been done to exploit CRQA for pattern matching of multidimensional, especially spatiotemporal, physiological signals. In this paper, we present a novel methodology in which CRQA statistics serve as measures of dissimilarity between pairs of signals and are subsequently used to uncover clusters within the data. This methodology is evaluated on a real dataset consisting of 3D spatiotemporal vectorcardiogram (VCG) signals from healthy and diseased patients. Experimental results show that Lmax, the length of the longest diagonal line in the CRP, yields the best-performing clustering that almost exactly matches the ground truth diagnoses of patients. Results also show that our proposed measure, Rτ max, which characterizes the maximum similarity between signals over all pairwise time-delayed alignments, outperforms all other tested CRQA measures (in terms of matching the ground truth) when the VCG signals are rescaled to reduce the effects of signal amplitude.

Metaheuristics on time series clustering problem: theoretical and empirical evaluation

Considering the literature and the importance of using of the metaheuristic techniques in time series data mining tasks, especially time series clustering (TsC), it seems lack of a comparative study of such techniques in terms of efficiency for TsC problem. Hence, we try to offer the possibility of theoretical and empirical evaluation of metaheuristic techniques in TsC problem. In fact, we follow two main goals by performing this theoretical and empirical evaluation. These goals include: at the first, we would like to prove the effective role of metaheuristic techniques to enhance the efficiency of TsC algorithms, eliminate some challenges of such algorithms (e.g., sensitivity to initialization of some primary parameters), and indicates the popularity of their use in TsC problems during the last years because of their characteristics. Second, we would like to offer the possibility of a comparison between some metaheuristic techniques to analyze the percentage of their effectiveness for enhancing the accuracy of clustering results in TsC problems. The comparative analysis of results of the empirical evaluation for ten standard time series data sets collected from the UCR time series data sets repository show three keys conclusions: (1) generally, metaheuristic techniques have provided reasonable results and significant improvement in terms of efficiency in TsC problem in all experiments due to the main characteristic of metaheuristics that is finding an approximate solution more quickly. But, it is concluded that fuzzy metaheuristic techniques based on population solution class (e.g., FATPSO and FPSO) to provide better results versus single solution class in TsC algorithms. (2) It is deducted that consuming maximum computational time presented by fuzzy metaheuristic techniques in comparison to other optimization techniques in the performed experiments. (3) The paper has proposed that a hybrid of fuzzy metaheuristics (e.g., FATPSO) and the base TsC algorithms can be considered as a more adequate choice to present better results in terms of accuracy in different application areas.

Sufficient conditions for the existence of a sample mean of time series under dynamic time warping

Time series averaging is an important subroutine for several time series data mining tasks. The most successful approaches formulate the problem of time series averaging as an optimization problem based on the dynamic time warping (DTW) distance. The existence of an optimal solution, called sample mean, is an open problem for more than four decades. Its existence is a necessary prerequisite to formulate exact algorithms, to derive complexity results, and to study statistical consistency. In this article, we propose sufficient conditions for the existence of a sample mean. A key result for deriving the proposed sufficient conditions is the Reduction Theorem that provides an upper bound for the minimum length of a sample mean.

Catch22: CAnonical Time-series CHaracteristics

Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147,000 time series) and using a filtered version of the hctsa feature library (4791 features), we introduce a set of 22 CAnonical Time-series CHaracteristics, catch22, tailored to the dynamics typically encountered in time-series data-mining tasks. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. catch22 captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, that facilitates feature-based time-series analysis for scientific, industrial, financial and medical applications using a common language of interpretable time-series properties.

A survey on time series motif discovery

Time series motifs are repeated subsequences in a long time series. Discovering time series motifs is an important task in time series data mining and this problem has received significant attention from researchers in data mining communities. In this paper, we intend to provide a comprehensive survey of the techniques applied for time series motif discovery. The survey also briefly describes a set of applications of time series motif in various domains as well as in high-level time series data mining tasks. We hope that this article can provide a broad and deep understanding of the time series motif discovery field.

A New Symbolization and Distance Measure Based Anomaly Mining Approach for Hydrological Time Series

Most of the time series data mining tasks attempt to discover data patterns that appear frequently. Abnormal data is often ignored as noise. There are some data mining techniques based on time series to extract anomaly. However, most of these techniques cannot suit big unstable data existing in various fields. Their key problems are high fitting error after dimension reduction and low accuracy of mining results. This paper studies an approach of mining time series abnormal patterns in the hydrological field. The authors propose a new idea to solve the problem of hydrological anomaly mining based on time series. They propose Feature Points Symbolic Aggregate Approximation (FP_SAX) to improve the selection of feature points, and then measures the distance of strings by Symbol Distance based Dynamic Time Warping (SD_DTW). Finally, the distances generated are sorted. A set of dedicated experiments are performed to validate the authors' approach. The results show that their approach has lower fitting error and higher accuracy compared to other approaches.

Distance Measure for Symbolic Approximation Representation with Subsequence Direction for Time Series Data Mining

A distance measure is proposed for time series data mining based on symbolic aggregate approximation (SAX) with direction representation. It aims at increasing lower bound tightness to Euclidean distance and decreasing the error rate of time series data mining tasks by adding the time series subsequence direction factor to original SAX. Experiments on public University of California, Riverside (UCR) time series datasets, which contain various time series data with diverse type, length, and size, demonstrate that the tightness of the proposed distance measure increases 17.54% on average when compared with that of original SAX, and classification error rates on SAX with direction representation are reduced by 16.22% in comparison with that of results obtained by original SAX. The proposed approach lowers the classification error rate and could be applied to other time series data mining tasks, such as clustering, query by content, and motif discovery.

A Similarity Measure Method for Symbolization Time Series

Similarity measure is the base task of time series data mining tasks. LCSS measure method has obvious limitations in the two different length time series selection of a linear function. The ELCS measure method is proposed to normalize the sequence, which introducing the scale factor to limit the search path of the similarity matrix. Experiment in hierarchical clustering algorithm shows that the improved measure makes up for the shortcomings of LCSS, improves the efficiency and accuracy of clustering and improves time complexity.

AN OVERVIEW OF SIMILARITY SEARCH IN TIME SERIES DATA

Time series data occur in many real life applications, ranging from science and engineering to business. In many of these applications, searching through large time series database based on query sequence is often desirable. Such similarity-based retrieval is also the basic subroutine in several advanced time series data mining tasks such as clustering, classification, finding motifs, detecting anomaly patterns, rule discovery and visualization. Although several different approaches have been developed, most are based on the common premise of dimensionality reduction and spatial access methods. This survey gives an overview of recent research and shows how the methods fit into a general framework of feature extraction.

Time Series Clustering Via RPCL Network Ensemble With Different Representations

Time series clustering provides underpinning techniques for discovering the intrinsic structure and condensing/summarizing information conveyed in time series, which is demanded in various fields ranging from bioinformatics to video content understanding. In this paper, we present an unsupervised ensemble learning approach to time series clustering by combining rival-penalized competitive learning (RPCL) networks with different representations of time series. In our approach, the RPCL network ensemble is employed for clustering analyses based on different representations of time series whenever available, and an optimal selection function is applied to find out a final consensus partition from multiple partition candidates yielded by applying various consensus functions for the combination of competitive learning results. As a result, our approach first exploits its capability of the RPCL rule in clustering analysis of automatic model selection on individual representations and subsequently applies ensemble learning for the synergy of reconciling diverse partitions resulted from the use of different representations and augmenting RPCL networks in automatic model selection and overcoming its inherent limitation. Our approach has been evaluated on 16 benchmark time series data mining tasks with comparison to state-of-the-art time series clustering techniques. Simulation results demonstrate that our approach yields favorite results in clustering analysis of automatic model selection.

Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle

Recently, the research on efficient extraction of previously unknown, frequently appearing patterns in a time-series data has received much attention. These patterns are called ‘motifs’. Motifs are useful for various time-series data mining tasks. In this paper, we propose a motif discovery algorithm to extract a motif that represents a characteristic pattern of the given data based on Minimum Description Length (MDL) principle. In addition, the algorithm can extract motifs from multi-dimensional time-series data by using Principal Component Analysis (PCA). In experimental evaluation, we show the efficiency of the motif discovery algorithm, and the usefulness of extracted motifs to various data mining tasks.