Introduction to Time Series and ForecastingSpringer, 2016-08-19 - 425 psl. This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains detailed instructions for the use of the professional version of the Windows-based computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series model-building are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user's own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Many additional special topics are also covered. New to this edition:
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Turinys
| 1 | |
2 Stationary Processes | 39 |
3 ARMA Models | 72 |
4 Spectral Analysis | 97 |
5 Modeling and Forecasting with ARMA Processes | 121 |
6 Nonstationary and Seasonal Time Series Models | 156 |
7 Time Series Models for Financial Data | 195 |
8 Multivariate Time Series | 227 |
11 Further Topics | 322 |
A Random Variables and Probability Distributions | 353 |
B Statistical Complements | 365 |
C Mean Square Convergence | 373 |
D Lévy Processes Brownian Motion and Itô Calculus | 375 |
E An ITSM Tutorial | 386 |
| 411 | |
| 418 | |
Kiti leidimai - Peržiūrėti viską
Introduction to Time Series and Forecasting Peter J. Brockwell,Richard A. Davis Ribota peržiūra - 2013 |
Introduction to Time Series and Forecasting Peter J. Brockwell,Richard A. Davis Peržiūra negalima - 2016 |
Pagrindiniai terminai ir frazės
AICC algorithm apply approximately ARMA process assume autocorrelation function autoregressive bounds Brockwell button calculation causal coefficients component computed conditional consider corresponding Davis defined Definition dependence described determine differenced discussed distribution equations estimated Example expressed Figure filter fitted follows forecasts function Gaussian given gives graph independent integrated ITSM linear maximum likelihood estimators mean squared error minimize multivariate normal observations obtained operator option PACF parameters particular polynomial prediction predictors Problem properties random variables recursions Remark representation residuals respectively result sample ACF sample mean satisfies seasonal Section sequence shown solution specified spectral density standard stationary statistic stochastic differential equation suggests transformed trend values variance vector white noise zero