Time Series Analysis and Forecasting
Chapter 1: Introduction to Time Series Data
Characteristics of Time Series Data
Components: Trend, Seasonality, Cyclical, Irregular
Loading and Handling Time Series in Pandas
Time Shifting, Lagging, and Rolling Windows
Visualizing Time Series Data
Hands-on Practice: Loading and Plotting Data
Quiz for Chapter 1
Chapter 2: Time Series Decomposition and Stationarity
Understanding Stationarity
Methods for Time Series Decomposition
Implementing Decomposition in Python
Testing for Stationarity: Visual Inspection
Statistical Tests for Stationarity (ADF Test)
Achieving Stationarity: Differencing
Hands-on Practice: Decomposition and Stationarity Tests
Quiz for Chapter 2
Chapter 3: Autocorrelation and Model Identification
Autocorrelation Function (ACF)
Partial Autocorrelation Function (PACF)
Plotting ACF and PACF in Python
Interpreting ACF/PACF for Model Selection
Hands-on Practice: ACF/PACF Plotting and Interpretation
Quiz for Chapter 3
Chapter 4: ARIMA Models for Forecasting
Autoregressive (AR) Models
Moving Average (MA) Models
Combining AR and MA: ARMA Models
Introducing Integration: ARIMA Models
Selecting ARIMA Order (p, d, q)
Fitting ARIMA Models in Python (statsmodels)
Model Diagnostics and Residual Analysis
Forecasting with ARIMA Models
Hands-on Practice: Building an ARIMA Model
Quiz for Chapter 4
Chapter 5: Handling Seasonality with SARIMA
Limitations of ARIMA with Seasonal Data
Introduction to Seasonal ARIMA (SARIMA)
Identifying Seasonal Components (ACF/PACF)
Selecting SARIMA Order (p, d, q)(P, D, Q)m
Fitting SARIMA Models in Python
Diagnosing SARIMA Models
Forecasting with SARIMA
Hands-on Practice: Building a SARIMA Model
Quiz for Chapter 5
Chapter 6: Model Evaluation and Selection
Need for Model Evaluation
Train-Test Split for Time Series
Common Evaluation Metrics (MAE, MSE, RMSE, MAPE)
Information Criteria (AIC, BIC)
Comparing Forecasts from Different Models
Visualizing Forecast Performance
Hands-on Practice: Evaluating Forecasts
Quiz for Chapter 6
Moving Average (MA) Models
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