Survey on time series motif discovery

Abstract

Last decades witness a huge growth in medical applications, genetic analysis, and in performance of manufacturing technologies and automatised production systems. A challenging task is to identify and diagnose the behavior of such systems, which aim to produce a product with desired quality. In order to control the state of the systems, various information is gathered from different types of sensors (optical, acoustic, chemical, electric, and thermal). Time series data are a set of real-valued variables obtained chronologically. Data mining and machine learning help derive meaningful knowledge from time series. Such tasks include clustering, classification, anomaly detection and motif discovery. Motif discovery attempts to find meaningful, new, and unknown knowledge from data. Detection of motifs in a time series is beneficial for, e.g., discovery of rules or specific events in a signal. Motifs provide useful information for the user in order to model or analyze the data. Motif discovery is applied to various areas as telecommunication, medicine, web, motion-capture, and sensor networks. This contribution provides a review of the existing publications in time series motif discovery along with advantages and disadvantages of existing approaches. Moreover, the research issues and missing points in this field are highlighted. The main objective of this focus article is to serve as a glossary for researchers in this field.

Mineração De Dados Aplicada À Séries Temporais Resultantes De Sinais Motores Da Doença De Parkinson

“…O algoritmo Motif Discovery visa encontrar padrões desconhecidos em uma série temporal sem qualquer dado prévio sobre seus locais ou formas [17]. A análise de regressão e k-nearest neighbor (KNN) são métodos muito usados, o KNN possui alta precisão e não apresenta rigor na parametrização dos dados.…”

Finding manoeuvre motifs in vehicle telematics

“…According to the authors, time-series motifs are over-represented subsequences in a time-series. The concept of motifs was first introduced in the genetics research as sequences of amino-acids in the DNA with biological significance [19] and, since then, the field of motif discovery for time-series has been receiving a lot of attention from the data mining community. Keogh and Lin [18] proposes to use a motif detection algorithm to find the set of motifs with the highest representation in the original time-series and then to apply the clustering algorithm directly to the motifs.…”

“…The behaviour constrain introduces the idea that segments in a motif should present the same general behaviour. Note that, depending on the application, distortions such as noise or time and amplitude shifts may be accepted [19]. The distance constrain goes a bit beyond by stating that all motif's segments need to have a distance smaller than a predefined radius R to the centre of the motif (i.e., the segment that represents that motif).…”

“…According to Torkamani and Lohweg [19], there is a considerable set of papers that address the variable-length problem by applying a fixed-length algorithm to a range of window sizes and then choosing the most representative motifs based on their motif definition and motif ranking schemes [27,28]. However, this strategy does not allow to have segments with different lengths in the same motif.…”

Self-Organizing Maps with Variable Input Length for Motif Discovery and Word Segmentation

“…Motifs are described in the literature as recurrent patterns, frequent tendencies, successions, forms, episodes, or frequent subsequences that occur in time series [1]. Motif Discovery methods search for previously unknown frequent patterns in a time series [2].…”