Time Series Motifs Research Articles

MULTI-OBJECTIVE GENETIC MOTIF DISCOVERY TECHNIQUE FOR TIME SERIES CLASSIFICATION

Time series is a sequence of continuous and unbounded group of data observations recorded from many applications. Time series motif discovery is an essential and important task in time series mining. Discovering motifs in time series has attracted the researcher's attention for efficient time series classification problems and several algorithms have been proposed to solve the problem. However, these algorithms depend on predefined parameters like support, confidence, and length of the motif and they are sensitive to the parameters. To overcome the challenge, this paper proposes a multi-objective genetic algorithm to discover a good trade-off between representative and interesting motif. The discovered motifs are validated for their potential interest in time series classification using nearest neighbour classifier. Extensive experiments show that the proposed approach can efficiently discover motifs with different lengths and more accurate than state-of-the-art time series techniques. The paper also demonstrates the efficiency of motif discovery in classifying the large time series medical data from MIT-BIH-arrhythmia database.

An efficient implementation of EMD algorithm for motif discovery in time series data

The extended motif discovery (EMD) algorithm, developed by Tanaka et al. (2005), is a well-known time series motif discovery algorithm which is based on minimum description length principle. One unique feature of the EMD algorithm is that it can determine the suitable length of the motif and hence does not require the length of the motif as a parameter supplied by user. Another interesting feature of the EMD is the lengths of each instances of a motif can be a bit different from each other and hence Tanaka et al. suggested that dynamic time warping (DTW) distance should be used to calculate the distances between the motif instances in this case. Due to the second feature, the EMD algorithm leads to a high computational complexity and is not easy to implement in practice. In this paper, we present an efficient implementation of the EMD algorithm in which we apply homothetic transformation to convert all pattern instances of different lengths into the same length so that we can easily calculate Euclidean distances between them. This modification accelerates the execution of the EMD remarkably and makes it easier to implement. Experimental results on seven real world time series datasets demonstrate the effectiveness of our EMD implementation method in time series motif discovery.

Particle swarm optimization for time series motif discovery

Efficiently finding similar segments or motifs in time series data is a fundamental task that, due to the ubiquity of these data, is present in a wide range of domains and situations. Because of this, countless solutions have been devised but, to date, none of them seems to be fully satisfactory and flexible. In this article, we propose an innovative standpoint and present a solution coming from it: an anytime multimodal optimization algorithm for time series motif discovery based on particle swarms. By considering data from a variety of domains, we show that this solution is extremely competitive when compared to the state-of-the-art, obtaining comparable motifs in considerably less time using minimal memory. In addition, we show that it is robust to different implementation choices and see that it offers an unprecedented degree of flexibility with regard to the task. All these qualities make the presented solution stand out as one of the most prominent candidates for motif discovery in long time series streams. Besides, we believe the proposed standpoint can be exploited in further time series analysis and mining tasks, widening the scope of research and potentially yielding novel effective solutions.

Exact multi-length scale and mean invariant motif discovery

Discovering approximately recurrent motifs (ARMs) in timeseries is an active area of research in data mining. Exact motif discovery is defined as the problem of efficiently finding the most similar pairs of timeseries subsequences and can be used as a basis for discovering ARMs. The most efficient algorithm for solving this problem is the MK algorithm which was designed to find a single pair of timeseries subsequences with maximum similarity at a known length. This paper provides three extensions of the MK algorithm that allow it to find the top K similar subsequences at multiple lengths using both the Euclidean distance metric and scale invariant normalized version of it. The proposed algorithms are then applied to both synthetic data and real-world data with a focus on discovery of ARMs in human motion trajectories.

Efficient Motif Discovery for Large-Scale Time Series in Healthcare

Analyzing time series data can reveal the temporal behavior of the underlying mechanism producing the data. Time series motifs, which are similar subsequences or frequently occurring patterns, have significant meanings for researchers especially in medical domain. With the fast growth of time series data, traditional methods for motif discovery are inefficient and not applicable to large-scale data. This work proposes an efficient M otif D iscovery method for La rge-scale t ime s eries (MDLats). By computing standard motifs, MDLats eliminates a majority of redundant computation in the related arts and reuses existing information to the maximum. All the motif types and subsequences are generated for subsequent analysis and classification. Our system is implemented on a Hadoop platform and deployed in a hospital for clinical electrocardiography classification. The experiments on real-world healthcare data show that MDLats outperform the state-of-the-art methods even in large time series.

Discovery of time series $$k$$ k -motifs based on multidimensional index

Time series motifs are frequently occurring but previously unknown subsequences of a longer time series. Discovering time series motifs is a crucial task in time series data mining. In time series motif discovery algorithm, finding nearest neighbors of a subsequence is the basic operation. To make this basic operation efficient, we can make use of some advanced multidimensional index structure for time series data. In this paper, we propose two novel algorithms for discovering motifs in time series data: The first algorithm is based on $$\hbox {R}^{*}$$R?-tree and early abandoning technique and the second algorithm makes use of a dimensionality reduction method and state-of-the-art Skyline index. We demonstrate that the effectiveness of our proposed algorithms by experimenting on real datasets from different areas. The experimental results reveal that our two proposed algorithms outperform the most popular method, random projection, in time efficiency while bring out the same accuracy.

Naïve Bayes Classifier for ECG Abnormalities Using Multivariate Maximal Time Series Motif

Analyze or diagnose of Cardiovascular activity under abnormal heart beat is extremely an intricate and vital job to the medical experts, made more complicated to a novice persons. Electrocardiogram is a way to measure or diagnose for research on human beings to spot heart disease by abnormal heart rhythms. These streaming medical signals can be well analyzed or diagnosed only with the prior knowledge. This paper proposes the methodology; Multivariate Maximal Time Series Motif with Naïve Bayes Classifier to classify the ECG abnormalities. The proposed model of predicting Time Series Motif is evaluated with the dataset contains the collection of ECG signals of patients recorded using Holter Monitor. The efficiency of the proposed work is proved by comparing the precision of existing with various Feature extraction Techniques.

Enumeration of time series motifs of all lengths

Time series motifs are repeated patterns in long and noisy time series. Motifs are typically used to understand the dynamics of the source because repeated patterns with high similarity evidentiall...

Rare time series motif discovery from unbounded streams

The detection of time series motifs , which are approximately repeated subsequences in time series streams, has been shown to have great utility as a subroutine in many higher-level data mining algorithms. However, this detection becomes much harder in cases where the motifs of interest are vanishingly rare or when faced with a never-ending stream of data. In this work we investigate algorithms to find such rare motifs. We demonstrate that under reasonable assumptions we must abandon any hope of an exact solution to the motif problem as it is normally defined; however, we introduce algorithms that allow us to solve the underlying problem with high probability.

Time series motif discovery: dimensions and applications

Time series motifs are repeated segments in a long time series that, if exist, carry precise information about the underlying source of the time series. Motif discovery in time series data has received significant attention in the data mining community since its inception, principally because, motif discovery is meaningful and more likely to succeed when the data is large.Algorithms for motif discovery generally deal with three aspects the definition of the motifs, domain based preprocessing, and finally, the algorithmic steps. Typical definitions of motifs signify the similarity or the support of the motifs. Domains impose preprocessing requirements to meaningful motif finding such as data alignment, interpolation, and transformation. Motif discovery algorithms vary based on exact or approximate evaluation of the definition. In addition, algorithms require different representations [Symbolic Aggregate approXimation (SAX), DFT etc.] and similarity measures [correlation, dynamic time warping (DTW) distance etc.] for time series segments. In this paper, we discuss these three facets in detail with examples taken from the literature.We briefly describe a set of applications of time series motif in various domains and elaborate on a certain application in entomology to analyze insect behavior.This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Technologies > Structure Discovery and Clustering

Temporal Analysis of Motif Mixtures Using Dirichlet Processes

In this paper, we present a new model for unsupervised discovery of recurrent temporal patterns (or motifs) in time series (or documents). The model is designed to handle the difficult case of multivariate time series obtained from a mixture of activities, that is, our observations are caused by the superposition of multiple phenomena occurring concurrently and with no synchronization. The model uses nonparametric Bayesian methods to describe both the motifs and their occurrences in documents. We derive an inference scheme to automatically and simultaneously recover the recurrent motifs (both their characteristics and number) and their occurrence instants in each document. The model is widely applicable and is illustrated on datasets coming from multiple modalities, mainly videos from static cameras and audio localization data. The rich semantic interpretation that the model offers can be leveraged in tasks such as event counting or for scene analysis. The approach is also used as a mean of doing soft camera calibration in a camera network. A thorough study of the model parameters is provided and a cross-platform implementation of the inference algorithm will be made publicly available.

Closed motifs for streaming time series classification

A streaming time series is a continuous and unbounded group of chronological observations that are found in many scientific and business applications. Motifs that are frequent subsequences are highly representative for the time series and play an important role in time series mining. Discovering motifs in time series has received much attention during recent years, and several algorithms have been proposed to solve this problem. However, these algorithms can only find motifs with a predefined length, which greatly affects their performance and practicality. Recent algorithms can discover motifs with different lengths, but require multiple scanning of the time series and are thus not applicable to streaming time series. In addition, it is difficult to determine the optimal length of interesting motifs; a suboptimal choice results in missing the key motifs or having too many redundant motifs. To overcome this challenge, we introduce the notion of a $$closed$$ closed motif; a motif is $$closed$$ closed if there is no motif with a longer length having the same number of occurrences. We propose a novel algorithm $$closedMotif$$ closedMotif to discover closed motifs in a single scan for streaming time series. We also use the nearest neighbor classifier with the most distinctive closed motifs to validate their potential in time series classification. Extensive experiments show that our approach can efficiently discover motifs with different lengths. In addition, our closed-motif-based classifier is shown to be more accurate than $$Logical\text{- }Shapelet$$ Logical - Shapelet , a state-of-the-art time series classifier. Finally, we demonstrate the scalability of $$closedMotif$$ closedMotif on several large datasets in diverse domains like video surveillance, sensor networks, and biometrics.

Significant motifs in time series

AbstractTime series motif discovery is the task of extracting previously unknown recurrent patterns from time series data. It is an important problem within applications that range from finance to health. Many algorithms have been proposed for the task of efficiently finding motifs. Surprisingly, most of these proposals do not focus on how to evaluate the discovered motifs. They are typically evaluated by human experts. This is unfeasible even for moderately sized datasets, since the number of discovered motifs tends to be prohibitively large. Statistical significance tests are widely used in the data mining communities to evaluate extracted patterns. In this work we present an approach to calculate time series motifs statistical significance. Our proposal leverages work from the bioinformatics community by using a symbolic definition of time series motifs to derive each motif's p‐value. We estimate the expected frequency of a motif by using Markov Chain models. The p‐value is then assessed by comparing the actual frequency to the estimated one using statistical hypothesis tests. Our contribution gives means to the application of a powerful technique—statistical tests—to a time series setting. This provides researchers and practitioners with an important tool to evaluate automatically the degree of relevance of each extracted motif. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining 5: 35–53, 2012

Significant motifs in time series

Time series motif discovery is the task of extracting previously unknown recurrent patterns from time series data. It is an important problem within applications that range from finance to health. ...

Combining motif information and neural network for time series prediction

Recent research works pay more attention to time series prediction, in which some time series data mining approaches have been exploited. In this paper, we propose a new method for time series prediction which is based on the concept of time series motifs. Time series motif is a previously unknown pattern appearing frequently in a time series. In the proposed approach, we first search for time series motif by using EP-C algorithm and then exploit motif information for forecasting in combination of a neural network model. Experimental results demonstrate our proposed method performs better than artificial neural network ANN in terms of prediction accuracy and time efficiency. Besides, our proposed method is more robust to noise than ANN.

Comprehensive and efficient discovery of time series motifs

Time series motifs are previously unknown, frequently occurring patterns in time series or approximately repeated subsequences that are very similar to each other. There are two issues in time series motifs discovery, the deficiency of the definition of K-motifs given by Lin et al. (2002) and the large computation time for extracting motifs. In this paper, we propose a relatively comprehensive definition of K-motifs to obtain more valuable motifs. To minimize the computation time as much as possible, we extend the triangular inequality pruning method to avoid unnecessary operations and calculations, and propose an optimized matrix structure to produce the candidate motifs almost immediately. Results of two experiments on three time series datasets show that our motifs discovery algorithm is feasible and efficient.

Large-scale IP network behavior anomaly detection and identification using substructure-based approach and multivariate time series mining

In this paper, a substructure-based network behavior anomaly detection approach, called WFS (Weighted Frequent Subgraphs), is proposed to detect the anomalies of a large-scale IP networks. With application of WFS, an entire graph is examined, unusual substructures of which are reported. Due to additional information given by the graph, the anomalies are able to be detected more accurately. With multivariate time series motif association rules mining (MTSMARM), the patterns of abnormal traffic behavior are able to be obtained. In order to verify the above proposals, experiments are conducted and, together with application of backbone networks (Internet2) Netflow data, show some positive results.

Identifying predictive multi-dimensional time series motifs: an application to severe weather prediction

We introduce an efficient approach to mining multi-dimensional temporal streams of real-world data for ordered temporal motifs that can be used for prediction. Since many of the dimensions of the data are known or suspected to be irrelevant, our approach first identifies the salient dimensions of the data, then the key temporal motifs within each dimension, and finally the temporal ordering of the motifs necessary for prediction. For the prediction element, the data are assumed to be labeled. We tested the approach on two real-world data sets. To verify the generality of the approach, we validated the application on several subjects from the CMU Motion Capture database. Our main application uses several hundred numerically simulated supercell thunderstorms where the goal is to identify the most important features and feature interrelationships which herald the development of strong rotation in the lowest altitudes of a storm. We identified sets of precursors, in the form of meteorological quantities reaching extreme values in a particular temporal sequence, unique to storms producing strong low-altitude rotation. The eventual goal is to use this knowledge for future severe weather detection and prediction algorithms.

A disk-aware algorithm for time series motif discovery.

Time series motifs are sets of very similar subsequences of a long time series. They are of interest in their own right, and are also used as inputs in several higher-level data mining algorithms including classification, clustering, rule-discovery and summarization. In spite of extensive research in recent years, finding time series motifs exactly in massive databases is an open problem. Previous efforts either found approximate motifs or considered relatively small datasets residing in main memory. In this work, we leverage off previous work on pivot-based indexing to introduce a disk-aware algorithm to find time series motifs exactly in multi-gigabyte databases which contain on the order of tens of millions of time series. We have evaluated our algorithm on datasets from diverse areas including medicine, anthropology, computer networking and image processing and show that we can find interesting and meaningful motifs in datasets that are many orders of magnitude larger than anything considered before.

A tree-construction search approach for multivariate time series motifs discovery

This paper examines an unsupervised search method to discover motifs from multivariate time series data. Our method first scans the entire series to construct a list of candidate motifs in linear time, the list is then used to populate a sparse self-similarity matrix for further processing to generate the final selections. The proposed algorithm is efficient in both running time and memory storage. To demonstrate its effectiveness, we applied it to search for repeating segments in both music and sensory data sets. The experimental results showed that the proposed method can efficiently detect repeating segments as compared to well-known methods such as self-similarity matrix search and symbolic aggregation approximation approaches.