Isbn: 9783846524688 - time series analysis of high speed wireless networks: forecasting network load using time series models (8 risultati)

Condizione: New. pp. 156.

Taschenbuch. Condizione: Neu. Time Series Analysis of High Speed Wireless Networks | Forecasting network load using time series models | S. Akhter Raza (u. a.) | Taschenbuch | 156 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846524688 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Modeling and forecasting of network traffic data presents a number of challenges in recent paradigm due to the volatility of data. There are various methods used for forecasting time series including AR, MA, ARMA, ARIMA, Fourier transform, ANN, and fuzzy logic. Wavelets technique has attracted the attention of researchers and is a rapidly growing area of research. Using the wavelet transformation, a multiresolution representation of a traffic signal is possible which breaks the signal into its shifted and scaled versions. This breaking up of signal is used for smoothing of time series to differentiate between signal and noise. The filtered and de-noised data is then further used to search time series models as possible candidates for forecasting. These models may be standard AR, ANN or fuzzy, etc to produce forecast that best estimate the mean and variance of actual traffic. Wavelets based Seasonal autoregressive moving average model is used to forecasting network traffic load at University of Karachi s High Speed Fibre Optics LAN which supports Wireless Computing. The new forecasting strategy incredibly improves the performance. 156 pp. Englisch.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Raza S. AkhterDr. Syed Akhter Raza received his Ph.D. from University of Karachi in the year 2011 and having about 20 years teaching experience at various institutions. His research interests include time series modeling, soft comput. …

Condizione: New. Print on Demand pp. 156 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condizione: New. PRINT ON DEMAND pp. 156.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Modeling and forecasting of network traffic data presents a number of challenges in recent paradigm due to the volatility of data. There are various methods used for forecasting time series including AR, MA, ARMA, ARIMA, Fourier transform, ANN, and fuzzy logic. Wavelets technique has attracted the attention of researchers and is a rapidly growing area of research. Using the wavelet transformation, a multiresolution representation of a traffic signal is possible which breaks the signal into its shifted and scaled versions. This breaking up of signal is used for smoothing of time series to differentiate between signal and noise. The filtered and de-noised data is then further used to search time series models as possible candidates for forecasting. These models may be standard AR, ANN or fuzzy, etc to produce forecast that best estimate the mean and variance of actual traffic. Wavelets based Seasonal autoregressive moving average model is used to forecasting network traffic load at University of Karachi's High Speed Fibre Optics LAN which supports Wireless Computing. The new forecasting strategy incredibly improves the performance.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 156 pp. Englisch. …

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Modeling and forecasting of network traffic data presents a number of challenges in recent paradigm due to the volatility of data. There are various methods used for forecasting time series including AR, MA, ARMA, ARIMA, Fourier transform, ANN, and fuzzy logic. Wavelets technique has attracted the attention of researchers and is a rapidly growing area of research. Using the wavelet transformation, a multiresolution representation of a traffic signal is possible which breaks the signal into its shifted and scaled versions. This breaking up of signal is used for smoothing of time series to differentiate between signal and noise. The filtered and de-noised data is then further used to search time series models as possible candidates for forecasting. These models may be standard AR, ANN or fuzzy, etc to produce forecast that best estimate the mean and variance of actual traffic. Wavelets based Seasonal autoregressive moving average model is used to forecasting network traffic load at University of Karachi s High Speed Fibre Optics LAN which supports Wireless Computing. The new forecasting strategy incredibly improves the performance. …