UsefulLinks
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
6.
Classical Forecasting Methods
6.1.
Naive Methods
6.1.1.
Naive Forecast
6.1.2.
Seasonal Naive
6.1.3.
Drift Method
6.1.4.
Random Walk Models
6.2.
Smoothing Methods
6.2.1.
Moving Averages
6.2.1.1.
Simple Moving Average
6.2.1.2.
Weighted Moving Average
6.2.1.3.
Exponentially Weighted Moving Average
6.2.2.
Exponential Smoothing
6.2.2.1.
Simple Exponential Smoothing
6.2.2.2.
Parameter Selection
6.2.2.3.
Initialization
6.2.3.
Holt's Method
6.2.3.1.
Linear Trend Model
6.2.3.2.
Level and Trend Smoothing
6.2.3.3.
Parameter Estimation
6.2.4.
Holt-Winters Method
6.2.4.1.
Additive Seasonality
6.2.4.2.
Multiplicative Seasonality
6.2.4.3.
Damped Trend Versions
6.3.
State Space Formulation
6.3.1.
ETS Framework
6.3.2.
Error Types
6.3.3.
Trend Types
6.3.4.
Seasonal Types
6.3.5.
Model Selection
6.4.
Forecasting with Classical Methods
6.4.1.
Point Forecasts
6.4.2.
Prediction Intervals
6.4.3.
Forecast Updating
6.4.4.
Performance Evaluation
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5. Autocorrelation and Dependence Structure
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7. ARIMA Modeling