ITSM [computer file] : / by Peter J. Brockwell and Richard A. Davis.

Por: Brockwell, Peter JColaborador(es): Davis, Richard AEditor: New York : Springer-Verlag, c1991Edición: Version 3.0Descripción: 3 computer disks ; 3 1/2-5 1/4 in. + 1 user's guideISBN: 0387974822; 3540974822Otro título: Additional title on disk: ITSM, an interactive time series modelling package for the PCTema(s): Time-series analysis -- SoftwareOtra clasificación: *CODIGO* Recursos en línea: Publisher description
Contenidos:
 Contents
Preface V
1 Introduction [1]
1.1 The Programs [1]
1.2 System Requirements [2]
1.2.1 Hard Disk Installation [2]
1.3 Creating Data Files [3]
2 PEST [4]
2.1 Getting Started [4]
2.1.1 Running PEST [4]
2.1.2 PEST Tutorial [5]
2.2 Preparing Your Data for Modelling [5]
2.2.1 Entering Data [6]
2.2.2 Filing Data [6]
2.2.3 Plotting Data [7]
2.2.4 Transforming Data [8]
2.3 Finding a Model for Your Data [15]
2.3.1 The ACF and PACF [15]
2.3.2 Entering a Model [18]
2.3.3 Preliminary Parameter Estimation [18]
2.3.4 The AICC Statistic [20]
2.3.5 Changing Your Model [21]
2.3.6 Parameter Estimation; the Gaussian Likelihood [22]
2.3.7 Optimization Results [27]
2.4 Testing Your Model [31]
2.4.1 Plotting the Residuals [31]
2.4.2 ACF/PACF of the Residuals [33]
2.4.3 Testing for Randomness of the Residuals [34]
2.5 Prediction [36]
2.5.1 Forecast Criteria [37]
2.5.2 Forecast Results [37]
2.5.3 Inverting Transformations [38]
2.6 Model Properties [40]
2.6.1 ARMA Models [41]
2.6.2 Model ACF, PACF [43]
2.6.3 Model Representations [44]
2.6.4 Generating Realizations of a Random Series [45]
2.6.5 Model Spectral Density [47]
2.7 Nonparametric Spectral Estimation [48]
2.7.1 Plotting the Periodogram [48]
2.7.2 Plotting the Cumulative Periodogram [51]
2.7.3 Fisher’s Test [52]
2.7.4 Smoothing to Estimate the Spectral Density [54]
3 SMOOTH [57]
3.1 Introduction [57]
3.2 Moving Average Smoothing [57]
3.3 Exponential Smoothing [58]
4 SPEC [61]
4.1 Introduction [61]
4.2 Bivariate Spectral Analysis [61]
4.2.1 Estimating the Spectral Density of Each Series [62]
4.2.2 Estimating the Absolute Coherency Spectrum [64]
4.2.3 Estimating the Phase Spectrum [66]
5 TRANS [68]
5.1 Introduction [68]
5.2 Computing Cross Correlations [68]
5.3 An Overview of Transfer Function Modelling [69]
5.4 Fitting a Preliminary Transfer Function Model [72]
5.5 Calculating Residuals from a Transfer Function Model [75]
5.6 LS Estimation and Prediction with Transfer Function Models [76]
6 ARVEC [83]
6.1 Introduction [83]
6.1.1 Multivariate Autoregression [83]
6.2 Model Selection with the AICC Criterion [85]
6.3 Forecasting with the Fitted Model [86]
7 ARAR [88]
7.1 Introduction [88]
7.1.1 Memory Shortening [88]
7.1.2 Fitting a Subset Autoregression [90]
7.2 Running the Program [91]
A Word6: A Screen Editor [95]
A.1 Basic Editing [95]
A.2 Alternate Keys [95]
A.3 Printing a File [96]
A.4 Merging Two or More Files [96]
A.5 Margins and Left and Centre Justification [96]
A.6 Tab Settings [97]
A.7 Block Commands [97]
A.8 Searching [98]
A.9 Special Characters [98]
A.10 Function Keys [99]
A. 11 Editing Information [99]
B Data Sets [100]
Index [103]
Forma de acceso: System requirements: IBM-compatible PC, PC/XT, or PC/AT (mathematics co-processor recommended); 540K RAM minimum for applications; MS-DOS; CGA, EGA, VGA, or Hercules graphics card.Resumen: Interactive time series modelling evolved from the programs for the IBM PC written to accompany the book, Time series: theory and methods. The analysis of time series data is an important aspect of data analysis across a wide range of disciplines, including statistics, economics, engineering, and the biological sciences. This package provides an introduction to time series anaysis along with the programs.
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Item type Home library Shelving location Call number Materials specified Status Date due Barcode
Libros Libros Instituto de Matemática, CONICET-UNS
Últimas adquisiciones 68 B864i (Browse shelf) Available A-9368

Title from disk label.

Same software on one 3 1/2 in. and two 5 1/4 in. disks.

La biblioteca no posee los disks.

Contents --
Preface V --
1 Introduction [1] --
1.1 The Programs [1] --
1.2 System Requirements [2] --
1.2.1 Hard Disk Installation [2] --
1.3 Creating Data Files [3] --
2 PEST [4] --
2.1 Getting Started [4] --
2.1.1 Running PEST [4] --
2.1.2 PEST Tutorial [5] --
2.2 Preparing Your Data for Modelling [5] --
2.2.1 Entering Data [6] --
2.2.2 Filing Data [6] --
2.2.3 Plotting Data [7] --
2.2.4 Transforming Data [8] --
2.3 Finding a Model for Your Data [15] --
2.3.1 The ACF and PACF [15] --
2.3.2 Entering a Model [18] --
2.3.3 Preliminary Parameter Estimation [18] --
2.3.4 The AICC Statistic [20] --
2.3.5 Changing Your Model [21] --
2.3.6 Parameter Estimation; the Gaussian Likelihood [22] --
2.3.7 Optimization Results [27] --
2.4 Testing Your Model [31] --
2.4.1 Plotting the Residuals [31] --
2.4.2 ACF/PACF of the Residuals [33] --
2.4.3 Testing for Randomness of the Residuals [34] --
2.5 Prediction [36] --
2.5.1 Forecast Criteria [37] --
2.5.2 Forecast Results [37] --
2.5.3 Inverting Transformations [38] --
2.6 Model Properties [40] --
2.6.1 ARMA Models [41] --
2.6.2 Model ACF, PACF [43] --
2.6.3 Model Representations [44] --
2.6.4 Generating Realizations of a Random Series [45] --
2.6.5 Model Spectral Density [47] --
2.7 Nonparametric Spectral Estimation [48] --
2.7.1 Plotting the Periodogram [48] --
2.7.2 Plotting the Cumulative Periodogram [51] --
2.7.3 Fisher’s Test [52] --
2.7.4 Smoothing to Estimate the Spectral Density [54] --
3 SMOOTH [57] --
3.1 Introduction [57] --
3.2 Moving Average Smoothing [57] --
3.3 Exponential Smoothing [58] --
4 SPEC [61] --
4.1 Introduction [61] --
4.2 Bivariate Spectral Analysis [61] --
4.2.1 Estimating the Spectral Density of Each Series [62] --
4.2.2 Estimating the Absolute Coherency Spectrum [64] --
4.2.3 Estimating the Phase Spectrum [66] --
5 TRANS [68] --
5.1 Introduction [68] --
5.2 Computing Cross Correlations [68] --
5.3 An Overview of Transfer Function Modelling [69] --
5.4 Fitting a Preliminary Transfer Function Model [72] --
5.5 Calculating Residuals from a Transfer Function Model [75] --
5.6 LS Estimation and Prediction with Transfer Function Models [76] --
6 ARVEC [83] --
6.1 Introduction [83] --
6.1.1 Multivariate Autoregression [83] --
6.2 Model Selection with the AICC Criterion [85] --
6.3 Forecasting with the Fitted Model [86] --
7 ARAR [88] --
7.1 Introduction [88] --
7.1.1 Memory Shortening [88] --
7.1.2 Fitting a Subset Autoregression [90] --
7.2 Running the Program [91] --
A Word6: A Screen Editor [95] --
A.1 Basic Editing [95] --
A.2 Alternate Keys [95] --
A.3 Printing a File [96] --
A.4 Merging Two or More Files [96] --
A.5 Margins and Left and Centre Justification [96] --
A.6 Tab Settings [97] --
A.7 Block Commands [97] --
A.8 Searching [98] --
A.9 Special Characters [98] --
A.10 Function Keys [99] --
A. 11 Editing Information [99] --
B Data Sets [100] --
Index [103] --

MR, REVIEW #

Interactive time series modelling evolved from the programs for the IBM PC written to accompany the book, Time series: theory and methods. The analysis of time series data is an important aspect of data analysis across a wide range of disciplines, including statistics, economics, engineering, and the biological sciences. This package provides an introduction to time series anaysis along with the programs.

System requirements: IBM-compatible PC, PC/XT, or PC/AT (mathematics co-processor recommended); 540K RAM minimum for applications; MS-DOS; CGA, EGA, VGA, or Hercules graphics card.

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