The Research Of Temporal Association Rules Mining Of Time Series

Temporal association rules of time series are the temporal constraining association among partly changes of time series. Partly changes of time series themselves have time sequence, so time order is a characteristic of the association. Time series have the characteristics of data denseness and stochastic fluctuation, and temporal association rules of partly changes are implied in the large data set, so the rules can be obtained only through data mining.The mining of temporal association rules of time series is a systematic engineering, which can be divided into time series data pre-processing, time series data compression, time series data similarity measure, the requirement of temporal association rules and the interpretation and evaluation of temporal association rules. The research on mining methods of temporal association rules has gained a lot, but is far from perfection. The main points are as follows.(1) In recognizing outlier, the method based on statistics is hard to gain the sample's distribution parameter, the method based on wavelet transform will change the authenticity of original time series, and the method based on likelihood ratio has a large amount of calculation.(2) In mining the classical temporal association rules, the time series are discredited into sequential patterns by the sliding window with the given length and steps. The frequent pattern will be acquired and it will end up with strengthened temporal association rules. Because the length and step for the sliding window are arbitrary, there is a lot of uncertainty in the result from the time series compression.(3) Similarity measure of time series is the base for acquiring the frequent pattern in sequential patterns, and also decides the obtainment of temporal association rules. The meta-pattern monotony distance and the meta-pattern vector distance both have some flaws in defining the meta-pattern, so the similarity measure of meta-pattern has some problems. And the existing methods of measuring series pattern's similarity cannot measure the series pattern's similarity of two different lengths.Temporal association rules of time series are practical valuable, but the existing mining methods have some flaws. So, the dissertation focuses on the improvement and perfection of the mining method of temporal association rules of time series, offering the theoretical models and empirical analysis, in order to gain more reliable temporal association rules from time series and help decision-making.The dissertation addresses the mining of temporal association rules. Aiming at the faultiness of every step, the author summarizes the existing relative research, then offers solutions and carries out empirical analysis. The dissertation can be divided into 8 chapters, the main content are as following.(1) Time Series Data Pre-processingTime series data pre-processing is the first step of mining temporal association rules that is how to clean the noise data in time series. In this part, the author first defines the noise data, and then sums up the existing recognition methods of outlier of time series, as well as analyzes their advantages and disadvantages. At last comes up with the recognition method of outlier of time series based on relative variance rate of time series.(2) Time Series Data CompressionTime series data compression is the second step of mining temporal association rules, which means how to transform time series into sequential patterns. Firstly the author analyzes the necessity, objective and meaning of compressing data in mining temporal association rules. And then analyzes the existing compressing ways, and then offers estimating system to value time series data compression. After comparative analysis, chooses time series data compression method, which is in favor of mining, and finally improves the reorganization of division point.(3) Time Series Data Similarity MeasureSimilarity measure of sequential patterns is the important content of temporal association rules of time series. Only the similarity among patterns is properly measured, the acquirement of frequent patterns in sequential patterns and temporal association rules can be successfully accomplished. The existing two methods have more or less disadvantages. Because the similarity among sequential patterns comes down to two models of different length, by using the method of measuring different dimensions distance, the author puts forward dynamic time warping distance means of sequential pattern.(4) Acquirement of Temporal Association RulesThe third step of mining temporal association rules is how to get frequent patterns from sequential patters, and then to build strengthened temporal association rules. In common temporal association rules, the objects may appear or not, and its frequency depends on the appearing times of objects and incidents. Because of the difference of time series pattern, the frequency cannot be decided by single model's appearing times, but by the amount of similar patterns. During the process of creating temporal association rules, according to the particularity of time series patterns, the author offers the layered means of getting temporal association rules and proves it.(5) Similarity of Time SeriesThe dissertation clarifies similarity of time series from two aspects. On the one hand, the dissertation studied the similarity of one-variety time series. Based on the summary of existing research on time series, the author puts forward the graphic similarity measure-to-measure similarity of time series and analyzes the method. On the other hand, the dissertation researches similarity of multivariate time series. Firstly the author analyzes the necessity of researching it, and then the difficulty in it, finally comes up with two ways to measure similarity of time series, based on matrix and synthesis attribution.(6)Mining Flat of Temporal association rules of time seriesThe mining flat of temporal association rules of time series uses JAVA as exploiting languages, and has 6 modules. It has several functions, such as loading data, time series data pre-processing, time series data compression, time series data similarity measure, the requirement of temporal association rules and the interpretation and evaluation of temporal association rules, etc. The dissertation proves every improvement by empirical analysis, and also realizes to mine temporal association rules from time series.Combining with theories of mining temporal association rules, the dissertation carries out systemic research on every step, from the first step, time series data pre-processing, to the last step, the interpretation and evaluation of temporal association rules. In every step, the author combs the existing research, tests the relative theoretical models, offers improvement and proves it. Because the mining of multivariate time series is a hot issue, the author discusses it in the last part. The innovations of the dissertation can be included as follows.(1) In time series data pre-processing, the author puts forwards recognition method of outlier noise data based on data variance ratio. Time series usually contains noise data, which will affect the mining temporal association rules, so it should be cleaned out before mining. Because time series compression is intolerant to outlier noise data, meanwhile the existence of outlier will affect the division of time series and representation of time series patterns, so identifying and deleting the outlier in time series will be one of the important works in time series data pre-processing. Whether a datum is the outlier, depends on its vibrancy with surrounding data. The author uses data variance ratio of time series data to estimate the vibrancy, and then offers recognition of outlier noise data.(2) In time series and similarity measure, the author comes up with Euclid distance method to measure the similarity of two meta-patterns and, and also brings forward dynamic time warping distance means to measure the similarity of two time series patterns. In mining temporal association rules, the meta-pattern monotony distance method and the meta-pattern vector distance method both are not suitable for getting frequent pattern when measuring the similarity between two meta patterns. Aiming the specialty of time series pattern, the dissertation offers weighted distance method of meta-pattern, and then comes up with dynamic time warping distance means, which can measure the similarity between two sequential patterns.(3) In the acquirement of temporal association rules, the author puts forwards the layered means. The time restriction of temporal association rules and…of association rules determines the difficulty of acquiring temporal association rules. In order to decrease the difficulty, we can divide the beforer of temporal association rules into different length and then mine, that is so called the layered mining of temporal association rules. Because of the difference in defining the frequent patterns, the method is different from other mining ways. Meanwhile because the method considers the beforer of different length, it has the unique advantages compared with other methods(4) When measuring the similarity between two time series, because the existing measure of one-variety time series ignores that time series is the function of time, the dissertation puts forward the graphic similarity measure. Meanwhile, in measuring similarity of multivariate time series, considering the storing way of multivariate time series is matrix, the dissertation offers two methods based on matrix norm to measure the similarity of multivariate time series and based on comprehensive attribute to measure the similarity of multivariate time series.