Time series analysis. (2007)

• Record Type: Book
• Further Information: Note: Henrik Madsen.
• Other Names: Madsen, Henrik, 1955-
• Contents: Preface ; Introduction ; Examples of time series; A first crash course; Contents and scope of the book; Multivariate random variables ; Joint and marginal densities; Conditional distributions; Expectations and moments; Moments of multivariate random variables; Conditional expectation; The multivariate normal distribution; Distributions derived from the normal distribution; Linear projections; Problems; Regression-based methods ; The regression model; The general linear model (GLM); Prediction; Regression and exponential smoothing; Time series with seasonal variations; Global and local trend model—an example; Problems; Linear dynamic systems ; Linear systems in the time domain; Linear systems in the frequency domain; Sampling; The z transform; Frequently used operators; The Laplace transform; A comparison between transformations; Problems; Stochastic processes ; Introduction; Stochastic processes and their moments; Linear processes; Stationary processes in the frequency domain; Commonly used linear processes; Non-stationary models; Optimal prediction of stochastic processes; Problems; Identification, estimation, and model checking ; Introduction; Estimation of covariance and correlation functions; Identification; Estimation of parameters in standard models; Selection of the model order; Model checking; Case study: Electricity consumption; Problems; Spectral analysis ; The periodogram; Consistent estimates of the spectrum; The cross-spectrum; Estimation of the cross-spectrum; Problems; Linear systems and stochastic processes ; Relationship between input and output processes; Systems with measurement noise; Input-output models; Identification of transfer-function models; Multiple-input models; Estimation; Model checking; Prediction in transfer-function models; Intervention models; Problems; Multivariate time series ; Stationary stochastic processes and their moments; Linear processes; The multivariate ARMA process; Non-stationary models ; Prediction; Identification of multivariate models; Estimation of parameters; Model checking; Problems; State space models of dynamic systems ; The linear stochastic state space model; Transfer function and state space formulations; Interpolation, reconstruction, and prediction; Some common models in state space form; Time series with missing observations; ML estimates of state space models; Problems; Recursive estimation ; Recursive LS; Recursive pseudo-linear regression (RPLR); Recursive prediction error methods (RPEM); Model-based adaptive estimation; Models with time varying parameters; Real life inspired problems ; Prediction of wind power production; Prediction of the consumption of medicine; Effect of chewing gum; Prediction of stock prices; Wastewater treatment: Using root zone plants; Scheduling system for oil delivery; Warning system for slippery roads; Statistical quality control; Modeling and control; Sales numbers; Modeling and prediction of stock prices; Adaptive modeling of interest rates; appendix A: The solution to difference equations ; appendix B: Partial autocorrelations ; appendix C: Some results from trigonometry ; appendix D: List of Acronyms ; appendix E: List of symbols ; Bibliography ; Index
• Publisher Details: Place of publication not identified : Chapman and Hall/CRC
• Publication Date: 2007
• Extent: 1 online resource, illustrations
• Subjects: 519.55; Time-series analysis
• Languages: English
• ISBNs: 9781420059687; 1420059688
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.