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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
Classical Forecasting Methods
Naive Methods
Naive Forecast
Seasonal Naive
Drift Method
Random Walk Models
Smoothing Methods
Moving Averages
Simple Moving Average
Weighted Moving Average
Exponentially Weighted Moving Average
Exponential Smoothing
Simple Exponential Smoothing
Parameter Selection
Initialization
Holt's Method
Linear Trend Model
Level and Trend Smoothing
Parameter Estimation
Holt-Winters Method
Additive Seasonality
Multiplicative Seasonality
Damped Trend Versions
State Space Formulation
ETS Framework
Error Types
Trend Types
Seasonal Types
Model Selection
Forecasting with Classical Methods
Point Forecasts
Prediction Intervals
Forecast Updating
Performance Evaluation
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5. Autocorrelation and Dependence Structure
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7. ARIMA Modeling