Titlebook: Applied Time Series Analysis and Innovative Computing; Sio-Iong Ao Book 2010 Springer Science+Business Media B.V. 2010 Computing Algorithm

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书目名称Applied Time Series Analysis and Innovative Computing影响因子(影响力)




书目名称Applied Time Series Analysis and Innovative Computing影响因子(影响力)学科排名




书目名称Applied Time Series Analysis and Innovative Computing网络公开度




书目名称Applied Time Series Analysis and Innovative Computing网络公开度学科排名




书目名称Applied Time Series Analysis and Innovative Computing被引频次




书目名称Applied Time Series Analysis and Innovative Computing被引频次学科排名




书目名称Applied Time Series Analysis and Innovative Computing年度引用




书目名称Applied Time Series Analysis and Innovative Computing年度引用学科排名




书目名称Applied Time Series Analysis and Innovative Computing读者反馈




书目名称Applied Time Series Analysis and Innovative Computing读者反馈学科排名

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Applied Time Series Analysis,rol of the system, or to obtain better forecasting of future values. Applied time series analysis consists of empirical models for analyzing time series in order to extract meaningful statistics and other properties of the time series data. Time series models have various forms and represent differe

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Real-Word Application II: Developing Innovative Computing Algorithms for Biological Time Series,ries data sets. An ensemble of the ENN and SVM models are described for how to further improve the prediction accuracy of the individual models. In order to provide the neural networks with explanation capabilities, a pedagogical rule extraction technique is considered for inferring the output of ou

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Real-Word Application III: Developing Innovative Computing Algorithms for Astronomical Time Series,and semi-automatic methods in the classifications/detections of the astrophysical objects. In fact, for surveys of millions of objects, it may not be possible to detect the desired objects by expert inspection alone. Quasars are interesting astrophysical objects that have been recently discovered mo

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https://doi.org/10.1007/978-3-319-12283-0moving average models. The autoregressive integrated moving average models are the general class of these models for forecasting a time series that can be stationarized by transformations such as differencing. In a frequency domain model, the analysis of mathematical functions or signals is conducte

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Epilepsy Towards the Next Decade knowledge metadata systems are presented. The advances in visualization, design and communication are described in Section 3.4. Section 3.5 is about the advances of innovative computing for time series problems, like retrieval, automatic classification, clustering, and automatic monitoring of time