1: Introduction to time series analysis
- Explain what a time series analysis is
- Describe how time series models work
- Demonstrate the main principles behind a time series forecasting model
2: Automatic forecasting with the Expert Modeler
- Examine fit and error
- Examine unexplained variation
- Examine how the Expert Modeler chooses the best fitting time series model
3: Measuring model performance
- Discuss various ways to evaluate model performance
- Evaluate model performance of an ARIMA model
- Test a model using a holdout sample
4: Time series regression
- Use regression to fit a model with trend, seasonality and predictors
- Handling predictors in time series analysis
- Detect and adjust the model for autocorrelation
- Use a regression model to forecast future values
5: Exponential smoothing models
- Types of exponential smoothing models
- Create a custom exponential smoothing model
- Forecast future values with exponential smoothing
- Validate an exponential smoothing model with future data
6: ARIMA modeling
- Explain what ARIMA is
- Learn how to identify ARIMA model types
- Use sequence charts and autocorrelation plots to manually identify an ARIMA model that fits the data
- Check your results with the Expert Modeler