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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
ARIMA Modeling
Box-Jenkins Methodology
Model Identification
Parameter Estimation
Diagnostic Checking
Forecasting
Model Refinement
Autoregressive Models
AR(1) Process
Higher-Order AR Models
Stationarity Conditions
Characteristic Equation
Yule-Walker Equations
Moving Average Models
MA(1) Process
Higher-Order MA Models
Invertibility Conditions
Theoretical Properties
ARMA Models
Combined AR and MA Components
Stationarity and Invertibility
Theoretical ACF and PACF
Wold Decomposition
Integrated Models
ARIMA Structure
Order of Integration
Differencing Operations
Unit Root Implications
Model Identification
ACF and PACF Analysis
Information Criteria
Parsimony Principle
Tentative Model Selection
Parameter Estimation
Maximum Likelihood Estimation
Least Squares Methods
Method of Moments
Numerical Optimization
Diagnostic Checking
Residual Analysis
Independence Tests
Ljung-Box Test
Normality Tests
Homoskedasticity Tests
Model Adequacy
Overfitting Detection
Goodness of Fit Measures
Forecasting with ARIMA
Minimum Mean Square Error Forecasts
Forecast Functions
Prediction Intervals
Forecast Updating
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6. Classical Forecasting Methods
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8. Seasonal ARIMA Models