FORECAST AND COMPUTER SIMULATIONS

1. FORECAST AND COMPUTER SIMULATIONS

2. Learning Objectives • Explain the steps in the forecasting process • Identify types of forecasting methods and their characteristics • Describe time series and causal forecasting models • Generate forecasts for different data patterns: level, trend, seasonality, and cyclical • Describe causal modeling using linear regression • Explain how forecasting models should be selected

3. Common Principles of Forecasting • Forecasts are rarely perfect • Forecasts are more accurate for grouped data than for individual items • Forecast are more accurate for shorter than longer time periods

4. Forecasting Steps • What needs to be forecast? • time horizon required • What data is available? • Select and test the forecasting model • Generate the forecast • Monitor forecast accuracy over time

5. Types of Forecasting Models • Qualitative methods: • Forecasts generated subjectively by the forecaster • Quantitative methods: • Forecasts generated through mathematical modeling

6. Qualitative Methods

7. Quantitative Methods • Time Series Models: • Assumes the future will follow same patterns as the past

8. Quantitative Methods • Causal Models: • Explores cause-and-effect relationships

9. Time Series Data Composition • Data = historic pattern + random variation • Historic pattern to be forecasted: • Level (long-term average) • Trend • Seasonality • Cycle • Random Variation cannot be predicted

10. Time Series Patterns

11. Components of Time Series Data

12. Time Series Models • Naive: • Simple Mean: • The average of all available data - good for level patterns • Moving Average: • The average value over a set time period (e.g.: the last four weeks) • Each new forecast drops the oldest data point & adds a new observation • Weighted Moving Average • Exponential Smoothing

13. Causal Models • Causal models establish a cause-and-effect relationship between independent and dependent variables • A common tool of causal modeling is linear regression: • Additional related variables may require multiple regression modeling

14. Linear Regression • Identifydependent (y) and independent (x) variables • Solve for the slope of the line • Solve for the y intercept • Develop your equation for the trend line Y=a + bX

15. Linear Regression Problem: A maker of golf shirts has been tracking the relationship between sales and advertising dollars. Use linear regression to find out what sales might be if the company invested $53,000 in advertising next year.

16. Measuring Forecast Error • Forecasts are never perfect • Measuring forecast error: • Note that over-forecasts = negative errors and under-forecasts = positive errors

17. A kind of under-forecasted weather phenomenum

18. Selecting the Right Forecasting Model • The amount & type of available data • Some methods require more data than others • Degree of accuracy required • Increasing accuracy means more data • Length of forecast horizon • Different models for 3 month vs. 10 years

19. Highlights • Forecasts are not perfect, are more accurate for groups than individual items, and are more accurate in the near term. • Five steps: what to forecast, evaluate appropriate data, select and test model, generate forecast, and monitor accuracy. • Two methods: qualitative methods are based on the subjective opinion of the forecaster and quantitative methods are based on mathematical modeling. • Time series models assume that all information needed is contained in the historical series of data, and causal models assume that the dependent variable is related to other variables in the environment.

20. Highlights (continued) • Four basic patterns of data: level, trend, seasonality, and cycles. Methods used to forecast the level of a time series are: naïve, simple mean, simple moving average, weighted moving average, and exponential smoothing. Separate models are used to forecast trends and seasonality. • Causal models, like linear regression, establish the relationship between the forecasted variable and another variable in the environment. • Useful measures of forecast error show under-forecasts or over-forecasts. • Factors to consider when selecting a model: amount and type of data available, degree of accuracy required, length of forecast horizon, and patterns present in the data.