Applied Time Series Analysis

<p>1. Time Series and Their Features<br>2. Transforming Time Series<br>3. ARMA Models for Stationary Time Series<br>4. ARIMA Models for Nonstationary Time Series<br>5. Unit Roots, Difference and Trend Stationarity, and Fractional Differencing<br>6. Breaking and Nonlinear Trends<br>7. An Introduction to Forecasting With Univariate Models<br>8. Unobserved Component Models, Signal Extraction, and Filters<br>9. Seasonality and Exponential Smoothing<br>10. Volatility and Generalized Autoregressive Conditional Heteroskedastic Processes<br>11. Nonlinear Stochastic Processes<br>12. Transfer Functions and Autoregressive Distributed Lag Modeling<br>13. Vector Autoregressions and Granger Causality<br>14. Error Correction, Spurious Regressions, and Cointegration<br>15. Vector Autoregressions With Integrated Variables, Vector Error Correction Models, and Common Trends<br>16. Compositional and Count Time Series<br>17. State Space Models<br>18. Some Concluding Remarks<br></p>