Predefined pattern detection from time series is an interesting and challenging task. In order to reduce its computational cost and increase effectiveness, a number of time series representation... Show morePredefined pattern detection from time series is an interesting and challenging task. In order to reduce its computational cost and increase effectiveness, a number of time series representation methods and similarity measures have been proposed. Most of the existing methods focus on full sequence matching, that is, sequences with clearly defined beginnings and endings, where all data points contribute to the match. These methods, however, do not account for temporal and magnitude deformations in the data and result to be ineffective on several real-world scenarios where noise and external phenomena introduce diversity in the class of patterns to be matched. In this paper, we present a novel pattern detection method, which is based on the notions of templates, landmarks, constraints and trust regions. We employ the Minimum Description Length (MDL) principle for time series preprocessing step, which helps to preserve all the prominent features and prevents the template from overfitting. Templates are provided by common users or domain experts, and represent interesting patterns we want to detect from time series. Instead of utilising templates to match all the potential subsequences in the time series, we translate the time series and templates into landmark sequences, and detect patterns from landmark sequence of the time series. Through defining constraints within the template landmark sequence, we effectively extract all the landmark subsequences from the time series landmark sequence, and obtain a number of landmark segments (time series subsequences or instances). We model each landmark segment through scaling the template in both temporal and magnitude dimensions. To suppress the influence of noise, we introduce the concept oftrust region, which not only helps to achieve an improved instance model, but also helps to catch the accurate boundaries of instances of the given template. Based on the similarities derived from instance models, we introduce the probability density function to calculate a similarity threshold. The threshold can be used to judge if a landmark segment is a true instance of the given template or not. To evaluate the effectiveness and efficiency of the proposed method, we apply it to two real-world datasets. The results show that our method is capable of detecting patterns of temporal and magnitude deformations with competitive performance. Show less
Today, virtually everything, from natural phenomena to complex artificial and physical systems, can be measured and the resulting information collected, stored and analyzed in order to gain new... Show moreToday, virtually everything, from natural phenomena to complex artificial and physical systems, can be measured and the resulting information collected, stored and analyzed in order to gain new insight. This thesis shows how complex systems often exhibit diverse behavior at different temporal scales, and that data mining methods should be able to cope with the multiple resolutions (scales) at the same time in order to fully understand the data at hand and extract useful information from it. Under these assumptions, we introduce novel data mining and visualization methods for large time series data collected from complex physical systems. In particular, we focus on three fundamental problems: the detection of multi-scale patterns, the recognition of recurrent events, and the interactive visualization of massive time series data. We evaluate our methods on a real-world scenario provided by InfraWatch, a Structural Health Monitoring project centered around the management and analysis of data collected by a large sensor network deployed on a Dutch highway bridge. The application of our methods resulted in the identification of the relevant scales of analysis in the InfraWatch data (and other datasets), the detection of the different recurring motifs and the visualization of terabytes of time series data interactively. Show less
According to the minimum description length (MDL) principle, data compression should be taken as the main goal of statistical inference. This stands in sharp contrast to making assumptions about an... Show moreAccording to the minimum description length (MDL) principle, data compression should be taken as the main goal of statistical inference. This stands in sharp contrast to making assumptions about an underlying ``true'' distribution generating the data, as is standard in the traditional frequentist approach to statistics. If the MDL premise of making data compression a fundamental notion can hold its ground, it promises a robust kind of statistics, which does not break down when standard, but hard to verify, assumptions are not completely satisfied. This makes it worthwhile to put data compression to the test, and see whether it really makes sense as a foundation for statistics. A natural starting point are cases where standard MDL methods show suboptimal performance in a traditional frequentist analysis. This thesis analyses two such cases. In the first case it is found that although the standard MDL method fails, data compression still makes sense and actually leads to the solution of the problem. In the second case we discuss a modification of the standard MDL estimator that has been proposed in the literature, which goes against its data compression principles. We also review the basic properties of R_nyi's dissimilarity measure for probability distributions. Show less
