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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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