Multiresolution motif discovery in time series

Time series motif discovery is an important problem with applications in a variety of areas that range from telecommunications to medicine. Several algorithms have been proposed to solve the problem. However, these algorithms heavily use expensive random disk accesses or assume the data can't into main memory. They only consider motifs at a single resolution and are not suited to interactivity. In this work, we tackle the motif discovery problem as an approximate Top-K frequent subsequence discovery problem. We fully exploit state of the art iSAX representation multiresolution capability to obtain motifs at diferent resolutions. This property yields interactivity, allowing the user to navigate along the Top-K motifs structure. This permits a deeper understanding of the time series database. Further, we apply the Top-K space saving algorithm to our frequent subsequences approach. A scalable algorithm is obtained that is suitable for data stream like applications where small memory devices such as sensors are used. Our approach is scalable and disk-eficient since it only needs one single pass over the time series database. We provide empirical evidence of the validity of the algorithm in datasets from diferent areas that aim to represent practical applications.(undefined

Classifying heart sounds using multiresolution time series motifs : an exploratory study

The aim of this work is to describe an exploratory study on the use of a SAX-based Multiresolution Motif Discovery method for Heart Sound Classification. The idea of our work is to discover relevant frequent motifs in the audio signals and use the discovered motifs and their frequency as characterizing attributes. We also describe different configurations of motif discovery for defining attributes and compare the use of a decision tree based algorithm with random forests on this kind of data. Experiments were performed with a dataset obtained from a clinic trial in hospitals using the digital stethoscope DigiScope. This exploratory study suggests that motifs contain valuable information that can be further exploited for Heart Sound Classification

Classifying heart sounds using SAX motifs, random forests and text mining techniques

In this paper we describe an approach to classifying heart sounds (classes Normal, Murmur and Extra-systole) that is based on the discretization of sound signals using the SAX (Symbolic Aggregate Approximation) representation. The ability of automatically classifying heart sounds or at least support human decision in this task is socially relevant to spread the reach of medical care using simple mobile devices or digital stethoscopes. In our approach, sounds are firrst pre-processed using signal processing techniques (decimate, low-pass filter, normalize, Shannon envelope). Then the pre-processed symbols are transformed into sequences of discrete SAX symbols. These sequences are subject to a process of motif discovery. Frequent sequences of symbols (motifs) are adopted as features. Each sound is then characterized by the frequent motifs that occur in it and their respective frequency. This is similar to the term frequency (TF) model used in text mining. In this paper we compare the TF model with the application of the TFIDF (Term frequency - Inverse Document Frequency) and the use of bi-grams (frequent size two sequences of motifs). Results show the ability of the motifs based TF approach to separate classes and the relative value of the TFIDF and the bi-grams variants. The separation of the Extra-systole class is overly dificult and much better results are obtained for separating the Murmur class. Empirical validation is conducted using real data collected in noisy environments. We have also assessed the cost-reduction potential of the proposed methods by considering a fixed cost model and using a cost sensitive meta algorithm.Portuguese Funds through the FCT - Fundacao para a Ciencia e a Tecnologia (proj. FCOMP-01-0124-FEDER-037281 and FCOMP-01-0124-FEDER-PEst-OE/EEI/UI0760/2014)

Automatically estimating iSAX parameters

The Symbolic Aggregate Approximation (iSAX) is widely used in time series data mining. Its popularity arises from the fact that it largely reduces time series size, it is symbolic, allows lower bounding and is space efficient. However, it requires setting two parameters: the symbolic length and alphabet size, which limits the applicability of the technique. The optimal parameter values are highly application dependent. Typically, they are either set to a fixed value or experimentally probed for the best configuration. In this work we propose an approach to automatically estimate iSAX’s parameters. The approach – AutoiSAX – not only discovers the best parameter setting for each time series in the database, but also finds the alphabet size for each iSAX symbol within the same word. It is based on simple and intuitive ideas from time series complexity and statistics. The technique can be smoothly embedded in existing data mining tasks as an efficient sub-routine. We analyze its impact in visualization interpretability, classification accuracy and motif mining. Our contribution aims to make iSAX a more general approach as it evolves towards a parameter-free method

Iterative variable gene discovery from whole genome sequencing with a bootstrapped multiresolution algorithm

In jawed vertebrates, variable (V) genes code for antigen-binding regions of B and T lymphocyte receptors, which generate a specific response to foreign pathogens. Obtaining the detailed repertoire of these genes across the jawed vertebrate kingdom would help to understand their evolution and function. However, annotations of V-genes are known for only a few model species since their extraction is not amenable to standard gene finding algorithms. Also, the more distant evolution of a taxon is from such model species, and there is less homology between their V-gene sequences. Here, we present an iterative supervised machine learning algorithm that begins by training a small set of known and verified V-gene sequences. The algorithm successively discovers homologous unaligned V-exons from a larger set of whole genome shotgun (WGS) datasets from many taxa. Upon each iteration, newly uncovered V-genes are added to the training set for the next predictions. This iterative learning/discovery process terminates when the number of new sequences discovered is negligible. This process is akin to “online” or reinforcement learning and is proven to be useful for discovering homologous V-genes from successively more distant taxa from the original set. Results are demonstrated for 14 primate WGS datasets and validated against Ensembl annotations. This algorithm is implemented in the Python programming language and is freely available at http://vgenerepertoire.org

Financial Time series: motif discovery and analysis using VALMOD

Motif discovery and analysis in time series data-sets have a wide-range of applications from genomics to finance. In consequence, development and critical evaluation of these algorithms is required with the focus not just detection but rather evaluation and interpretation of overall significance. Our focus here is the specific algorithm, VALMOD, but algorithms in wide use for motif discovery are summarised and briefly compared, as well as typical evaluation methods with strengths. Additionally, Taxonomy diagrams for motif discovery and evaluation techniques are constructed to illustrate the relationship between different approaches as well as inter-dependencies. Finally evaluation measures based upon results obtained from VALMOD analysis of a GBP-USD foreign exchange (F/X) rate data-set are presented, in illustration

Classificação de sons urbanos usando motifs e MFCC

A classificação automática de sons urbanos é importante para o monitoramento ambiental. Este trabalho apresenta uma nova metodologia para classificar sons urbanos, que se baseia na descoberta de padrões frequentes (motifs) nos sinais sonoros e utiliza-los como atributos para a classificação. Para extrair os motifs é utilizado um método de descoberta multi-resolução baseada em SAX. Para a classificação são usadas árvores de decisão e SVMs. Esta nova metodologia é comparada com outra bastante utilizada baseada em MFCC. Para a realização de experiências foi utilizado o dataset UrbanSound disponível publicamente. Realizadas as experiências, foi possível concluir que os atributos motif são melhores que os MFCC a discriminar sons com timbres semelhantes e que os melhores resultados são conseguidos com ambos os tipos de atributos combinados. Neste trabalho foi também desenvolvida uma aplicação móvel para Android que permite utilizar os métodos de classificação desenvolvidos num contexto de vida real e expandir o dataset.The automatic classification of urban sounds is important for environmental monitoring. This work presents a new method to classify urban sounds based on frequent patterns (motifs) in the audio signals and using them as classification attributes. To extract the motifs, a multiresolution discovery based on SAX is used. For the classification itself, decision trees and SVMs are used. This new method is compared with another largely used based on MFCCs. For the experiments, the publicly available UrbanSound dataset was used. After the experiments, it was concluded that motif attributes are better to discriminate sounds with similar timbre and better results are achieved with both attribute types combined. In this work was also developed a mobile application for Android which allows the use of the developed classifications methods in a real life context and to expand the dataset