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CASt online resources

Time series analysis: Introductions
The Analysis of Time Series: An Introduction
    by Chris Chatfield (6th ed, 2003). A readable presentation of time series theory and practice. Covers autoregressive ARMA/ARIMA models, Fourier spectral analysis, linear systems, likelihood-based state-space models, and the Kalman filter, nonlinear models, multivariate time series, and long-memory models.

An Introduction to Time Series and Forecasting
    by Peter J. Brockwell, Richard A. Davis & P. J. Rockwell (2nd ed, 2002). Undergraduate-level monograph on time series with Windows-based software. Includes regression with stationary autoregressive ARMA/ARIMA models, nonstationary ARCH/GARCH models, multivariate time series, maximum likelihood state space modeling, time series errors, Poisson data. Methods include Burg, Hannan-Rissanen, EM, Holt-Winters, Kalman, and other algorithms.

A First Course in Statistics for Signal Analysis
    by Wojbor A. Woyczunski (2006). Text for engineering students on random signal processing. Covers Fourier transforms and power spectra, stationarity and autocorrelation, bandpass and other filters, Gaussian signals, and discrete signals.

Time series analysis: More advanced
A Wavelet Tour of Signal Processing
    by Chibli G. Mallat & Stephane Mallat (2nd ed, 1999). Comprehensive graduate textbook of wavelet theory and engineering applications with Matlab-based software. Topics include Fourier methods, wavelet & related transforms, analog & digital methods, noise removal, deconvolution, signal and image compression, singularity and edge detection, multifractal analysis, and time-frequency problems.

Wavelet Methods for Time Series Analysis
    by Donald B. Percival & Andrew T. Walden (2000). Graduate-level text with emphasis on applications in the physical sciences. Includes introductions to wavelets and Fourier theory, development of the discrete wavelet transform, stochastic processes, wavelet variance, long memory processes, and signal estimation (thresholding, scaling, shrinkage).

Time Series Analysis by State Space Methods
    by Durbin & S. J. Koopman (2002). Monograph on parametric maximum likelihood modeling of time series using state space methods, with applications in econometrics. Topics include linear Gaussian models, filtering & smoothing, maximum likelihood estimation, Kalman filtering, Bayesian analysis & Gibbs sampling, nonlinear & nonGaussian models.

Smoothness Priors Analysis of Time Series
    by Genshiro Kitagawa & Will Gersch (1996). Monographs on Bayesian approaches to modeling of complex time series in the context of state space modeling, including applications in the physical sciences. Covers linear Gaussian state space modeling, nonstationary models and variance, multivariate time series, inhomogeneous Poisson processes, quasi-periodic processes, and non-linear smoothing.

Stochastic processes
Introduction to Probability Models
    by Sheldon M. Ross (9th ed, 2006).  Popular undergraduate-level textbook on stochastic processes. Topics include introduction to probability theory and random variables, conditional probability, Markov chains, Poisson processes, renewal theory, queueing theory, reliability theory, Brownian motion & stationary processes, and simulation techniques.

Stochastic Processes & Models
    by David Stirzaker (2005). Well-written undergraduate-level text from Oxford introducing stochastic processes. Covers random variables; martingales and Poisson processes, Markov chains, Monte Carlo simulation, birth processes and queues, and diffusion processes.

Statistical Analysis of Stochastic Processes in Time
    by James K. Lindsey (2004). Moderately advanced text on stochastic processes with R code available. Topics include state space modeling, survival processes, Markov chains, dynamic models (hidden Markov models & Kalman filtering), doubly stochastic processes, change points, autoregression, spectral analysis, growth curves, and repeated measurements.

Poisson Processes
    by J. F. C. Kingman (1993). Slim, insightful volume arguing for the importance of Poisson processes in the theory of random processes. Discusses Poisson processes, various theorems, Poisson processes on a line, marked Poisson processes, Cox processes, stochastic geometry, and the Poisson-Direchlet distribution.
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