|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
Kiburm Song, Minho Ryu and Kichun Lee
Numerous dimensionality-reducing representations of time series have been proposed in data mining and have proved to be useful, especially in handling a high volume of time series data. Among them, widely used symbolic representations such as symbolic ag...
ver más
|
|
|
|
|
|
|
|
|
|
|
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...
ver más
|
|
|
|
|
|
|
|
|
|
|
Yufeng Yu, Dingsheng Wan, Qun Zhao and Huan Liu
Anomalous patterns are common phenomena in time series datasets. The presence of anomalous patterns in hydrological data may represent some anomalous hydrometeorological events that are significantly different from others and induce a bias in the decisio...
ver más
|
|
|
|
|
|