ITSM [computer file] : / by Peter J. Brockwell and Richard A. Davis.
Editor: 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 descriptionContents 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]
Item type | Home library | Shelving location | Call number | Materials specified | Status | Date due | Barcode |
---|---|---|---|---|---|---|---|
Libros | Instituto de Matemática, CONICET-UNS | Libros ordenados por tema | 68 B864i (Browse shelf) | Available | A-9368 |
Browsing Instituto de Matemática, CONICET-UNS shelves, Shelving location: Libros ordenados por tema Close shelf browser
68 B858 Operating system principles / | 68 B858a The architecture of concurrent programs / | 68 B864 Thinking FORTH : | 68 B864i ITSM | 68 B866 Le langage Algol : | 68 B873 Automatic data processing / | 68 B877 Macro processors and techniques for portable software / |
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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