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1. Introduction to Time Series
1.1. What are Time Series?
1.2. Best Sources for Public Datasets
1.3. Time Series Cross-Validation
1.4. Time Series Visualizations
1.5. What Are ACF and PACF?
1.6. Cross-Correlation in Time Series
2. Missing Data in Time Series
2.1. Types of Missing Data
2.2. Identifying Patterns of Missingness
2.3. Handling Missing Values
2.4. Advanced Imputation with K-Nearest Neighbors (KNN)
3. Seasonality and Stationarity
3.1. Understanding Seasonality
3.2. Seasonal-Trend Decomposition using LOESS (STL)
3.3. Additive vs. Multiplicative Decomposition
3.4. Robust STL
3.5. Stationarity and Non-Stationarity in Time-Series Analysis
4. Classical Time Series Models
4.1. Time Series Smoothing
4.2. Foundations of Exponential Smoothing: Level & Trend
4.3. Advanced Exponential Smoothing: Seasonality & Statistical Framework
4.4. ARIMA Models
4.5. ARIMA Models in Practice
4.6. SARIMA and SARIMAX
4.7. Automated ARIMA Models
5. Outlier Detection in Time Series
5.1. Outlier Detection in Time Series
5.2. Outlier Classification by Scope
5.3. Outlier Classification by Seasonality
5.4. Visual Methods for Outlier Detection
5.5. Descriptive Statistics-Based Methods for Outlier Detection in Time Series
5.6. Time Series Modeling-Based Methods for Outlier Detection
6. References
Index
