E7: Forecasting and Time Series Analysis
Benefits:
- Understanding influence of autocorrelation on interpretation of control charts
- Identifying limitations of smoothing methods as forecasting tools
- Interpreting forecasts from decomposition models
- Using ARIMA models to forecast seasonal and non-seasonal data
- Incorporating effects of deliberate policy changes or unexpected events into forecasts
- Experience in developing and evaluating forecasts
Key Topics:
- Review of advanced regression analysis
- Basic time series concepts
- Smoothing methods
- Decomposition methods (level, trend, seasonal components)
- Autocorrelation and cross correlation
- ARIMA (Auto Regressive Integrated Moving Average) models
- Seasonal forecasting
- Forecasting with leading indicators
- Intervention analysis
- Evaluating forecast performance
Duration:
One (1) week
Prerequisite:
Experience with multiple regression analysis at Six Sigma Black Belt level or equivalent
Who Should Attend:
- Individuals designing or interpreting control chart
- Demand Planners and others with forecasting responsibilitie
- Mentors of Black Belts and Green Belts
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