Research On Mining And Similar Searching In Time Series Database

Time series is a kind of important data existing in a lot of fields, such as stock, weather, etc. With time moving, this data of time series will explode increasing. So it is important and challenging subject to research how discovery valuable knowledge in large-scale time series database, and how to search based similarity while user give a graphic query pattern. These researches will help us to discover changing or developing principle of things, support to decision-making, etc.The thesis addresses several key technical problems of pattern mining and its search based similarity in time series, which covers feature patterns and relationship patterns mining, pattern search based similarity in time series and stream time series and issues concerning application system implementation oriented to analysis. Major contributions of this thesis include:1. Research of mining feature patterns in time seriesA novel method is proposed to discovery frequent pattern from time series. Different to exiting methods, it first segments time series based on a series of perceptually important points, and then time series are converted into meaningful symbols sequences in terms of domain knowledge and the relative scope of each linear segment. After that, we designed a new data model, called Inter-Related Successive Trees IRST, to find frequent patterns from multiple time series without generation lots of candidate patterns. Experiment illustrates that the method is simpler and more flexible, efficient and useful, compared with the previous methods.2. Research of Mining Relationship Patterns in Multiple Time SeriesAn algorithm for discovery frequent patterns in multiple time series will be proposed. In this algorithm, firstly the states relationship between in time series is represented to Allen temporal logic, then use a sliding windows to examine the order or occur relationship of states and obtain a particularly sequence. On the basis of the sequence, we developed a called GIRST model to achieve finding the frequent relationship patterns in multiple time series. Experiments shows, compared with the previous methods, the method is more simple, efficient and more applied value.3. Research of similar search in time seriesA novel method is proposed to fast search similar pattern in time series using fulltext index technique. The method first segments time series based on a series of perceptually important points, use segment dynamic time warping distance as measurement, and then time series are converted into meaningful symbol sequences in terms of the segment's features and MATH categorization. After that, use above index model-IRST, to achieve fast similarity retrieval in multiple time series. The method is proved not any false dismiss in the theory and experiments show it has more efficient search and allows different lengths matching, compared with the previous methods.