forecastit 1. introduction to forecasting
DESCRIPTION
This lesson begins with background information about forecasting and continues discussing the forecasting process. We concentrate on four main steps: set an objective, build model, evaluate model, and use model.TRANSCRIPT
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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 Forecasting Methods
• 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 to evaluate 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• Method selection steps:
– Select dependent variable– Select units– Select time frame– Select what method to use– Adjusted units if needed to fit method– Adjust how many periods to forecast depending on method
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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
Forecasting Steps1. Set an objective2. Build model3. Evaluate model4. Use model
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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
(dependent), and GDP(independent), interest rate(independent)…
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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: F-Test P-Value < 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