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Time Series Analysis
A time series is an ordered sequence of values of a variable at equally spaced time intervals. Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for.
- Author
- Alexander Aue
- Publisher
- University of California, Davis
- Source
- LibreTexts
- Language
- EN
- Category
- Basic Sciences
Contents· 24 sections
- 1: Basic Concepts in Time Series
- 1.1: Introduction and Examples
- 1.2: Stationary Time Series
- 1.3: Eliminating Trend Components
- 1.4: Eliminating Trend and Seasonal Components
- 1.5: Assessing the Residuals
- 1.6: Summary
- 2: The Estimation of Mean and Covariances
- 2.1: Estimation of the Mean
- 2.2: Estimation of the Autocovariance Function
- 3: ARMA Processes
- 3.1: Introduction to Autoregressive Moving Average (ARMA) Processes
- 3.2: Causality and Invertibility
- 3.3: The PACF of a Causal ARMA Process
- 3.4: Forecasting
- 3.5: Parameter Estimation
- 3.6: Model Selection
- 3.7: Summary
- 4: Spectral Analysis
- 4.1: Introduction to Spectral Analysis
- 4.2: The Spectral Density and the Periodogram
- 4.3: Large Sample Properties
- 4.4: Linear Filtering
- 4.5: Summary