Useful Links
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
Seasonal ARIMA Models
Seasonal Patterns in Data
Identifying Seasonality
Seasonal Differencing
Seasonal Unit Roots
SARIMA Model Structure
Notation (p,d,q)(P,D,Q)s
Non-Seasonal Components
Seasonal Components
Seasonal Period
Model Identification
Seasonal ACF and PACF
Combined Pattern Recognition
Model Selection Strategies
Estimation and Diagnostics
Parameter Estimation
Seasonal Residual Analysis
Model Validation
Forecasting with SARIMA
Seasonal Forecast Generation
Long-Term Forecasting
Seasonal Prediction Intervals
Special Cases
Airline Model
Seasonal Random Walk
Multiplicative Seasonal Models
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9. Advanced Univariate Models