Time series motifs statistical significance

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. Many algorithms have been proposed for the task of eficiently 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 bioinformatics and association rules mining communities to evaluate the 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.(undefined

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

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

Time series motif discovery

Programa doutoral MAP-i em Computer ScienceTime series data are daily produced in massive proportions in virtually every field. Most of the data are stored in time series databases. To find patterns in the databases is an important problem. These patterns, also known as motifs, provide useful insight to the domain expert and summarize the database. They have been widely used in areas as diverse as finance and medicine. Despite there are many algorithms for the task, they typically do not scale and need to set several parameters. We propose a novel algorithm that runs in linear time, is also space efficient and only needs to set one parameter. It fully exploits the state of the art time series representation (SAX _ Symbolic Aggregate Approximation) technique to extract motifs at several resolutions. This property allows the algorithm to skip expensive distance calculations that are typically employed by other algorithms. We also propose an approach to calculate time series motifs statistical significance. Despite there are many approaches in the literature to find time series motifs e_ciently, surprisingly there is no approach that calculates a 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. Finally, we propose an approach to automatically derive the Symbolic Aggregate Approximation (iSAX) time series representation's parameters. This technique is widely used in time series data mining. Its popularity arises from the fact that it is symbolic, reduces the dimensionality of the series, allows lower bounding and is space efficient. However, the need to set the symbolic length and alphabet size parameters limits the applicability of the representation since the best parameter setting is highly application dependent. Typically, these are either set to a fixed value (e.g. 8) or experimentally probed for the best configuration. The technique, referred as 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 the simple and intuitive ideas of time series complexity and standard deviation. The technique can be smoothly embedded in existing data mining tasks as an efficient sub-routine. We analyse the impact of using AutoiSAX in visualization interpretability, classification accuracy and motif mining results. Our contribution aims to make iSAX a more general approach as it evolves towards a parameter-free method.As séries temporais são produzidas diariamente em quantidades massivas em diferentes áreas de trabalho. Estes dados são guardados em bases de dados de séries temporais. Descobrir padrões desconhecidos e repetidos em bases de dados de séries temporais é um desafio pertinente. Estes padrões, também conhecidos como motivos, dão uma nova perspectiva da base de dados, ajudando a explorá-la e sumarizá-la. São frequentemente utilizados em áreas tão diversas como as finanças ou a medicina. Apesar de existirem diversos algoritmos destinados à execução desta tarefa, geralmente não apresentam uma boa escalabilidade e exigem a configuração de vários parâmetros. Propomos, neste trabalho, a criação de um novo algoritmo que executa em tempo linear e que é igualmente eficiente em termos de memória usada, necessitando apenas de um parâmetro. Este algoritmo usufrui da melhor técnica de representação de séries temporais para extrair motivos em várias resoluções (SAX). Esta propriedade permite evitar o cálculo de distâncias que têm um custo computacional muito elevado, cálculo este geralmente presente noutros algoritmos. Nesta tese também fazemos uma proposta para calcular a significância estatística de motivos em séries temporais. Apesar de existirem muitas propostas para a detecção eficiente de motivos em séries temporais, surpreendentemente não existe nenhuma aproximação para calcular a sua significância estatística. A nossa proposta é enriquecida pelo trabalho da área bioinformática, sendo usada uma definição simbólica de motivo para derivar o seu respectivo p-value. Estimamos a frequência esperada de um motivo usando modelos de cadeias de Markov. O p-value associado a um teste estatístico é calculado comparando a frequência real com a frequência estimada de cada padrão. A nossa contribuição permite a aplicação de uma técnica poderosa, testes estatísticos, para a área das séries temporais. Proporciona assim, aos investigadores e utilizadores, uma ferramenta importante para avaliarem, de forma automática, a relevância de cada motivo extraído dos seus dados. Por fim, propomos uma metodologia para derivar de forma automática os parâmetros da representação de séries temporais Symbolic Aggregate Approximation (iSAX). Esta técnica é vastamente utilizada na área de Extracção de Conhecimento em séries temporais. A sua popularidade surge associada ao facto de ser simbólica, de reduzir o tamanho das séries, de permitir aproximar a Distância Euclidiana nas séries originais e ser eficiente em termos de espaço. Contudo, a necessidade de definir os parâmetros comprimento da representação e tamanho do alfabeto limita a sua utilização na prática, uma vez que o parâmetro mais adequado está dependente da área em causa. Normalmente, estes são definidos quer para um valor fixo (por exemplo, 8). A técnica, designada por AutoiSAX, não só extrai a melhor configuração do parâmetro para cada série temporal da base de dados como consegue encontrar a dimensão do alfabeto para cada símbolo iSAX dentro da mesma palavra. Baseia-se em ideias simples e intuitivas como a complexidade das séries temporais e no desvio padrão. A técnica pode ser facilmente incorporada como uma sub-rotina eficiente em tarefas existentes de extracção de conhecimento. Analisamos também o impacto da utilização do AutoiSAX na capacidade interpretativa em tarefas de visualização, exactidão da classificação e na qualidade dos motivos extraídos. A nossa proposta pretende que a iSAX se consolide como uma abordagem mais geral à medida que se vai constituindo como uma metodologia livre de parâmetros.Fundação para a Ciência e Tecnologia (FCT) - SFRH / BD / 33303 / 200

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570