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This research paper focuses on data mining in time series and its applications on financial data. Data-mining attempts to analyze time series and extract valuable information about pattern periodicity, which might be concealed by substantial amounts of unformatted, random information. Such information, however, is of great importance as it can be used to forecast future behavior. In this paper, a new methodology is introduced aiming to utilize Suffix Arrays in data mining instead of the commonly used data structure Suffix Trees. Although Suffix Arrays, normally, require high storage capacity, the algorithm proposed allows them to be constructed in linear time. The methodology is also extended to detect repeated patterns in time series with time complexity of. This, combined with the capability of external storage, creates a critical advantage, for an overall efficient data mining and analysis regarding the construction of time series data structure and periodicity detection. The test results, presented below demonstrate the applicability and effectiveness of the proposed technique.