Study Of Key Technologies On Abnormal Pattern Mining In Time Series

Study of key technologies on abnormal pattern mining in time series has important academic value and practical significance. In order to deal with the continuity, non-linear and high-dimension of time series, this thesis aims to explore a new way to mine abnormal pattern of times series. The methods of abnormal detection based on classfication and dimensionality reduction of characters attributes are studied, which are applied to anomaly detection of coal mine gas datas. At the same time, the solutions and definition of layout of multisensor system are proposed. An abnormal detection software system of gas based on time series data mining is developed. The main contents are as follows:An inverse classification method of quantitative attributes was presented, which overcomes the disadvantage of most inverse classification algorithms address discrete attributes. The algorithm puts emphasis on analysis of the training samples and the test samples, the main idea is: firstly, a group of feature attributes are selected by using feature selection algorithm; then, the quantitative attributes are discretized by using discretization algorithms, and the inverted statistics are constructed on the training samples; finally, the test samples are analyzed by using the inverted statistics, and the method is applied to abnormal detection and estimating the missing values. Experimental results on IRIS and Ecoli datasets show that the accuracy of classification, the average relative deviation and the maximum relative deviation are better than KNN(k-Nearest Neighbors) and ISGNN(Iteration Self-Generated Neural Network).In order to overcome the low accuracy of anomaly detection based on manifold learning methods. Two supervised manifold learning algorithms are proposed, named PSLLE (Supervised Locally Linear Embedding based on Problitity) and PQSLLE (Quick Supervised Locally Linear Embedding based on Problitity). They both adjust the distance between samples by calculating the bias problitity of samples in order to preserve the hidden classlabels. So the test samples could be deal with by supervised manifold learning algorithm. Experimental results on IRIS, Wine and Semeion Handwritten Digit datasets show that the accuracy of anomaly detection after dimensionality reduction by proposed algorithm is better than LLE(Locally Linear Embedding) and SLLE(Supervised Locally Linear Embedding).Anomaly pattern multisensor time series is got by analysis of sensor values, which shows correlation of datas, and lacks physical correlation analysis. In order to overcome the disadvantage, the solutions and definition of layout of multisensor system are proposed. Formal description of the solutions is given, and the multi-objective optimization models of multi-sensor separately in one, tow, three dimensional spaces are established.Solve the problem of anomaly detection about coal mine gas by using methods of abnormal pattern mining in time series. Based on above research results, an abnormal detection system for gas data is developed, which includes three modules, abnormal detection, estimating the missing values and manifold learning. The test results show that the software runs well.