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Zhenwen He, Chi Zhang and Yunhui Cheng
Time series data typically exhibit high dimensionality and complexity, necessitating the use of specific approximation methods to perform computations on the data. The currently employed compression methods suffer from varying degrees of feature loss, le...
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Eoin Cartwright, Martin Crane and Heather J. Ruskin
As the availability of big data-sets becomes more widespread so the importance of motif (or repeated pattern) identification and analysis increases. To date, the majority of motif identification algorithms that permit flexibility of sub-sequence length d...
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Zhenwen He, Chunfeng Zhang, Xiaogang Ma and Gang Liu
Time series data are widely found in finance, health, environmental, social, mobile and other fields. A large amount of time series data has been produced due to the general use of smartphones, various sensors, RFID and other internet devices. How a time...
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Zhenwen He, Shirong Long, Xiaogang Ma and Hong Zhao
A large amount of time series data is being generated every day in a wide range of sensor application domains. The symbolic aggregate approximation (SAX) is a well-known time series representation method, which has a lower bound to Euclidean distance and...
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