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Statistics
Time Series Analysis
1. Introduction to Time Series
2. Foundational Concepts and Data Preparation
3. Descriptive Analysis and Decomposition
4. Stationarity and Unit Root Analysis
5. Autocorrelation and Dependence Structure
6. Classical Forecasting Methods
7. ARIMA Modeling
8. Seasonal ARIMA Models
9. Advanced Univariate Models
10. Volatility Modeling
11. Multivariate Time Series
12. State Space Models and Kalman Filtering
13. Machine Learning for Time Series
14. Forecasting Evaluation and Model Selection
15. Practical Forecasting Considerations
16. Software and Implementation
Autocorrelation and Dependence Structure
Measuring Temporal Dependence
Covariance and Correlation
Lag Relationships
Serial Dependence
Autocorrelation Function
Definition and Properties
Sample ACF
Theoretical ACF Patterns
Confidence Intervals
Interpretation Guidelines
Partial Autocorrelation Function
Definition and Calculation
Sample PACF
Theoretical PACF Patterns
Model Identification Uses
Cross-Correlation Analysis
Cross-Correlation Function
Lead-Lag Relationships
Prewhitening
Spurious Correlation
Spectral Analysis
Frequency Domain Analysis
Periodogram
Spectral Density
Fourier Transform
Nonlinear Dependence
Higher-Order Moments
Conditional Heteroskedasticity
Nonlinear Correlation Measures
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4. Stationarity and Unit Root Analysis
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6. Classical Forecasting Methods