Abstract
Time delay is a general phenomenon in industrial process. Accurate evaluation on delay is important in data preprocessing when mine manufactory process data. As typical streaming time series, sensors’ data of industrial process have attracted much attention recently. A new concept , trend similarity search, is proposed based on raw monotony between two industrial process variables. The new concept is for those two time series which are similar only in trend but dissimilar in shape, whereas similarity search may not do well in such condition. An algorithm DelayMine is also proposed to mine delay between two interrelated time series by trend similarity search. Moreover, the DelayMine is extended to online algorithm for processing streaming time series. The properties and performance of DelayMine is demonstrated through experiments both on systems with steady and time-varying delay.
This work is supported by National Natural Science Foundation of China (60421002).
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© 2006 Springer-Verlag Berlin Heidelberg
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Gu, H., Rong, G. (2006). Mining Delay in Streaming Time Series of Industrial Process. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_79
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DOI: https://doi.org/10.1007/11811305_79
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
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