Forecast It 1. Introduction to the Forecasting Process
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Transcript of Forecast It 1. Introduction to the Forecasting Process
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8/9/2019 Forecast It 1. Introduction to the Forecasting Process
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Introduction to Forecasting
Lesson #1
Introduction to Forecasting
Using quantitative methods
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Introduction to Forecasting
Quantitative vs. Qualitative forecastingmethods
Quantitative
Universal Meaning
Widely Used in Business
Easy to Evaluate
Vs.
Qualitative
Build on Personal Experience
No Data Needed
Hard to Measure
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Introduction to Forecasting
Quantitative Methods Take upon multiple forms
Linear Regression: Y = a + b * x
Exponential Smoothing: F(t+1) = a * A (t) + ( 1 a) * F(t)
Moving Average, Multivariable, Etc.
Have Multiple Purposes
Forecasting
Trend, Seasonality, and Cyclical Estimation
Evaluating Variable Relationships
And more
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Introduction to Forecasting
Role of statistics in quantitative forecasting Each Variable is assumed to be Independent of each other, and
random.
The central limit theorem states that if a variable has 30 or more
observations, we can use the normal distribution approximation toevaluate its mean and variance.
Enables us to test the statistical significance of the model and its
coefficients.
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Introduction to Forecasting
Method Selection Each Forecasting Method has its own characteristics.
Each Method can answer different questions.
Some Methods should only be used in specific situations.
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Introduction to Forecasting
Model Building Most Methods try to fit themselves to the data.
Minimizing the Sum of Squared Errors.
A sum of square difference of actual data points compared to
model produced data points.
Creates the best fit model using that specific forecasting method.
Model statistics are produced for model evaluate.
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Introduction to Forecasting
Model Evaluation Looking at the statistics of a model help us determine if the model
makes sense or not and how accurate it is.
The same Statistics are used for all type of methods
Enables us to Compare Multiple Models to find the best models
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Introduction to Forecasting
4 Forecasting Steps1. Set an objective
2. Build models backed by theory
3. Evaluate models
4. Use best models
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Introduction to Forecasting
1. Objective Setting Keep it Simple
Set a clear objective such as:
Find Best Forecast for Gas prices
Find the Overall trend of Gas prices from multiple forecast
models
Find Seasonal Indices for Gas prices
Find Relationships (or lack of) between variables Gas prices
(Dep.), and GDP(Indp.), Interest Rate(Indp.)
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Introduction to Forecasting
2. Building Models Understand the type of data you have.
Does it have a trend, seasonality, or cyclicality.
Select the appropriate methods to use given the objective and data
type.
Use (economic, financial, est.) theory to back your model
Start with simple models.
Build on good simple models
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Introduction to Forecasting
3. Evaluating Models Statistical Significance
F-Test
F-Test P-Value (Rule of Thumb: Need Below 0.05)
Accuracy (The lower the better except R2)
SSE: Sum of Squared Errors
RMSE: Root Mean Square Error
MAPE: Mean Average Percentage Error
R2/ Adjusted R2: % Error Captured by Model
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Introduction to Forecasting
4. Using Models Use Best Models for:
Forecasting
Understanding Trend, Seasonality, and cyclicality.
Understanding Relationships between different variables
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