Copyright 2010 DeepThought, Inc. 1

Course Outline

Course Outline

By ForecastIT

Copyright 2010 DeepThought, Inc. 2

Course Outline

Lessons• Lesson #1: Introduction to Forecasting• Lesson #2: Intro to Linear Regression & Model Statistics• Lesson #3: Intro to Simple Exponential Smoothing

• Lesson #4: Intro to Holt’s Exponential Smoothing• Lesson #5: Intro to Winters’ Exponential Smoothing• Lesson #6: Multi-Variable Linear Regression• Lesson #7: Decomposition• Lesson #8: Data Transformation• Lesson #9: Evaluating Multiple Models

Copyright 2010 DeepThought, Inc. 3

Course Outline

Objectives• Understand the forecasting process• Understand the steps in the forecasting process• Understand the tasks in each step of the process• Understand how to use multiple forecasting methods• Understand how to compare multiple models

Copyright 2010 DeepThought, Inc. 4

Course Outline

Statistical Analysis• Statistical significance of model built

– Math Talk: Hypothesis testing if the models estimated coefficients are statistically different from 0

– Plain English: With what certainty can we be confident that the model we build is relevant

– We use the F-Test P-Value to tell us the confidence level of the model. The lower the F-Test P-Value the more confidence it becomes

• Accuracy/Error– Use multiple statistics to help us determine how accurate the

model is and how bad is its error

Copyright 2010 DeepThought, Inc. 5

Course Outline

Comparing Multiple Models• To find the best models, comparing multiple models is essential• Statistics have universal meaning, enabling us to compare multiple

models easily• Best models have high statistical significance and lower error

values compared to their counter parts