Forecasting in R
Time required: 4 Hours
In this course we will introduce you to different methods for forecasting future events. While the topic of forecasting can be applied to many business and non-business domains, much of the discussion will focus on making financial forecasts.
We will briefly review subjective forecasting methods and evaluate the advantages and disadvantages of such approaches so that you have a better appreciation for empirical forecasting methods. We will then introduce you to time-series data, and why that type of data is useful for financial analytics.
The remainder of the course will focus on how to evaluate forecasts and forecasting models. We will conclude by introducing you to linear regression, a statistical technique for creating a linear function that can be used to make forecasts.
Upon successful completion, you will be able to:
Explore subjective and data-driven forecasting methods
Consider how forecasts can be applied to business application
Evaluate the quality of a forecast
Evaluate the quality of a model that is used to make forecast
Apply regression to create explanatory forecasts in R
Introduction Video: Forecasting in Practice with Jose Rodriguez
Introduction to the Skill
Glossary
Subjective Forecasting
Business Forecasting and Time Series Data
Introduction to Financial Analytics
Knowledge Check 1
Distance Calculations Between Forecasts and Actual Observations
Metrics Used for Evaluating Financial Forecast Accuracy
Knowledge Check 2
Introduction to Forecasting -- Average Method
Introduction to Forecasting -- Naive Method
Introduction to Forecasting -- Linear Regression
Introduction to Forecasting -- R Example
Knowledge Check 3
Instructions
Exercise Files
Debriefing
Conclusion
Final Quiz
Survey Instructions
Feedback Survey
Survey Verification
Next Steps