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Titlebook: ITSM for Windows; A User’s Guide to Ti Peter J. Brockwell,Richard A. Davis Book 1994 Springer Science+Business Media New York 1994 Augmente

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書目名稱ITSM for Windows
副標(biāo)題A User’s Guide to Ti
編輯Peter J. Brockwell,Richard A. Davis
視頻videohttp://file.papertrans.cn/461/460538/460538.mp4
圖書封面Titlebook: ITSM for Windows; A User’s Guide to Ti Peter J. Brockwell,Richard A. Davis Book 1994 Springer Science+Business Media New York 1994 Augmente
描述The analysis of time series data is an important aspect of data analysis across a wide range of disciplines, including statistics, mathematics, business, engineering, and the natural and social sciences. This package provides both an introduction to time series analysis and an easy-to-use version of a well-known time series computing package called Interactive Time Series Modelling. The programs in the package are intended as a supplement to the text .Time. .Series: Theory and Methods., 2nd edition, also by Peter J. Brockwell and Richard A. Davis. Many researchers and professionals will appreciate this straightforward approach enabling them to run desk-top analyses of their time series data. Amongst the many facilities available are tools for: ARIMA modelling, smoothing, spectral estimation, multivariate autoregressive modelling, transfer-function modelling, forecasting, and long-memory modelling. This version is designed to run under Microsoft Windows 3.1 or later. It comes with two diskettes: one suitable for less powerful machines (IBM PC 286 or later with 540K available RAM and 1.1 MB of hard disk space) and one for more powerful machines (IBM PC 386 or later with 8MB of RAM an
出版日期Book 1994
關(guān)鍵詞Augmented Reality; Fitting; Likelihood; Simulation; Time series; best fit; correlation; data analysis; stati
版次1
doihttps://doi.org/10.1007/978-1-4612-2676-5
isbn_softcover978-0-387-94337-4
isbn_ebook978-1-4612-2676-5
copyrightSpringer Science+Business Media New York 1994
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Introduction,(each on a 3 1/2″ diskette). The system requirements for . are fewer than for . (see Section 1.2), however . can handle larger data sets (univariate series with up to 20000 observations and multivariate series with up to 10000 observations of each of 11 components). Both versions of the package contain the programs listed below.
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ARAR,– 287). The latter was found to perform extremely well in the forecasting competition of Makridakis, the results of which are described in the book. The ARARMA scheme has a further advantage over most standard forecasting techniques in being more readily automated.
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ARVEC,ust be stored as an ASCII file such that row t contains the m components, .= {.,…,.)′, each separated by at least one blank space. (The sample size n can be at most 700 for . and 10000 for ..) The value of n will then be printed on the screen and you will be given the option of plotting the component series.
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SMOOTH,creen of the title page, you will be asked if you wish to . or to .. Type . and select the name of the data file to be smoothed. After following the program prompts, you will see the Smoothing Menu which provides a choice of three smoothing methods for the series {X., . = 1,…, n}.
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