forecastit course outline
DESCRIPTION
The following lessons present the knowledge needed to become proficient in creating, evaluating, comparing, and using quantitative methods for forecasting purposes. Each lesson will introduce a concept or method followed by an example.TRANSCRIPT
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
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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
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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
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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