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Mining sequences and patterns in time series data streams have a tremendous growth of interest in todays world. The rapid progress of data collection and the web technologies yield tremendous growth of flowing data in various complex forms that need to be analyzed on fly. Traditional data mining methods typically that require to process data by scanning it… (More)
Mining sequences and patterns in time series data streams is fast becoming a common practice in today’s world. The rapid progress of data collection and web technologies yields tremendous growth of flowing data in various complex forms that need to be analyzed in real time. Traditional data mining methods that typically require the process data to be… (More)
Time series data stream mining has attracted considerable research interest in recent years. Pattern discovery is a challenging problem in time series data stream mining. Because the data update continuously and the sampling rates may be different, dynamic time warping (DTW)-based approaches are used to solve the pattern discovery problem in time series… (More)
In contrast to general time series analysis, only a few numbers of studies are devoted to subsequence pattern matching methods for financial time series. In this paper, we compare the processing time and accuracy of three well-known pattern matching methods from financial time series domain and two pattern matching methods from general time series area. Our… (More)
Metaheuristics have lately gained popularity among researchers. Their underlying designs are inspired by biological entities and their behaviors, e.g. schools of fish, colonies of insects, and other land animals etc. They have been used successfully in optimization applications ranging from financial modeling, image processing, resource allocations, job… (More)
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