Efficient Similarity Search over Future Stream Time Series

With the advance of hardware and communication technologies, stream time series is gaining ever-increasing attention due to its importance in many applications such as financial data processing, network monitoring, Web click-stream analysis, sensor data mining, and anomaly detection. For all of these applications, an efficient and effective similarity search over stream data is essential. Because of the unique characteristics of the stream, for example, data are frequently updated and real-time response is required, the previous approaches proposed for searching through archived data may not work in the stream scenarios. Especially, in the cases where data often arrive periodically for various reasons (for example, the communication congestion or batch processing), queries on such incomplete time series or even future time series may result in inaccuracy using traditional approaches. Therefore, in this paper, we propose three approaches, polynomial, Discrete Fourier Transform (DFT), an...

• Type: Journal
• Year: 2008
• Where: TKDE
• Authors: Xiang Lian, Lei Chen 0002
• Keywords: Future Time Series, Stream, Time Series, TKDE 2008