Applied time series analysis: a practical guide to modeling and forecasting

Content: Time series and their features --
Transforming time series --
ARMA models for stationary time series --
ARIMA models for nonstationary time series --
Unit roots, difference and trend stationarity, and fractional differencing --
Breaking and nonlinear trends --
An introduction to forecasting with univariate models --
Unobserved component models, signal extraction, and filters --
Seasonality and exponential smoothing --
Volatility and generalized autoregressive conditional heteroskedastic processes --
Nonlinear stochastic processes --
Transfer functions and autoregressive distributed lag modeling --
Vector autoregressions and Granger causality --
Error corection, spurious regressions, and cointegration --
Vector autoregressions with integrated variables, vector error correction models, and common trends --
Compositional and count time series --
State space models.