Applied Time Series Analysis and Innovative Computing by Sio-Iong Ao

By Sio-Iong Ao

Applied Time sequence research and cutting edge Computing contains the utilized time sequence research and cutting edge computing paradigms, with frontier software reports for the time sequence difficulties in response to the new works on the Oxford collage Computing Laboratory, collage of Oxford, the collage of Hong Kong, and the chinese language college of Hong Kong. The monograph was once drafted while the writer used to be a post-doctoral fellow in Harvard institution of Engineering and technologies, Harvard college. It presents a scientific advent to using leading edge computing paradigms as an investigative device for purposes in time sequence research. themes lined comprise Frequency area, Correlation, Smoothing, Periodogram, Autoregression, ARIMA types, Discrimination research, Clustering research, issue research, Dynamic Fourier research, Random Coefficient Regression, Discrete Fourier rework, cutting edge Computing Algorithms, wisdom Extraction, huge complicated Databases, Modeling and Simulations, Integration of undefined, platforms and Networks, Grid Computing, Visualization, layout and communique, company Time sequence functions, organic Time sequence purposes, and Astronomical Time sequence functions. Applied Time sequence research and leading edge Computing offers the country of paintings of super advances in utilized time sequence research and leading edge computing paradigms and likewise serves as a superb reference paintings for researchers and graduate scholars engaged on utilized time sequence research and leading edge computing paradigms.

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One ongoing research direction in time series data mining is to develop algorithms that can identify similar temporal patterns in a collection of time series. The identification and retrieval of similar time series pattern may lead to the discovery of hidden information in time series database (Keogh et al. 2001). Saeed and Mark (2006) proposed a novel temporal similarity metric based on a transformation of time series data into an intuitive symbolic representation. After the symbolic transformation, traditional information retrieval algorithms based on a vector–space model can be utilized.

A serious difficulty for the application of KDD algorithms to large complex databases is the scalability problem, which may lead to excessive processing time (Cohen 1995). Parallelism is used to speed up the queries that otherwise would takes a long time to process. It can keep the processing times constant, even if the amount of data items increases (Reuter 1999). Query optimizers were designed when optimizing complex queries. Tao et al. (2003) proposed a new similarity-based optimization technique.

Wu et al. (1998) applied the frequency spectrum PCA for modeling the sound frequency distribution features. The frequency spectra of the found were treated as a vector in a high-dimensional frequency feature space. The PCA was used to compute the variance distribution for the frequency vectors, with the largest eigenvalues accounting for the most variance within the data set. The proposed method is shown to be simple and reliable for acoustic identification. 5 Dynamic Fourier Analysis Dynamic Fourier analysis is useful for the non-stationary time series analysis, by giving a local-time representation of the spectrum (Shumway and Stoffer 2006).

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