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Course Description

Forecasting can be found in every corner of the business world today. When done in tandem with accurate time series analysis, it enables sound prediction of future values. In this course, you will explore the use of time series analysis and the four components of time series data. Consider, there are a number of time series that may require forecasting but do not have any discernible trend, such as a stable product environment or a very short timeframe. In this course you will continue exploring forecasting by examining stationary time series and the situations in which they most often occur and practice forecasting techniques and stationary time series analysis. You will then examine stationary data where no substantial change is taking place. Lastly, you will move to data that is changing. A layer of complexity can be added to forecasting in the form of seasonality, where the time series being studied regularly changes with each season. This added element must be considered in any prediction of future periods.

It is recommended to only take this course if you have completed Presenting Quantitative Data, Descriptive Statistics for Business, Making Predictions Using Statistical Probability, Inferential Statistics, and Multivariable Comparisons or have equivalent experience.

Faculty Author

Cindy van Es

Benefits to the Learner

  • Explore the use of time series analysis and the four components of time series data
  • Discuss the role of forecasting in your organization
  • Apply the tools of analysis, including graphs and diagnostic measures
  • Explore forecasting by examining stationary time series and the situations in which they most often occur
  • Define autocorrelation and the key role that it plays in forecasting stationary data
  • Practice forecasting techniques and stationary time series analysis
  • Analyze linear data: data that either increases or decreases at a steady rate
  • Observe forecasting performed with an original data source and a single variable
  • Explore the relationship between two time series and the methods used for these projections
  • Consider curvilinear models and then develop your own time series forecasts
  • Define the factors that cause seasonality
  • Identify the steps to follow in forecasting a seasonal time series, which include quantifying and separating seasonal influences and then applying that information to your forecasting
  • Follow those steps yourself in computing seasonal forecasts

Target Audience

  • Entry level to executive professionals looking to uncover insights from data
  • Students who are pre-MBA or considering earning an MBA
  • Individuals interested in moving into an analyst role
  • Individuals seeking to leverage statistical or analytic skills
  • New, emerging, and experienced leaders
  • Consultants
  • Analysts and researchers
  • Entrepreneurs

Applies Towards the Following Certificates

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Enroll Now - Select a section to enroll in

Type
2 week
Dates
Sep 23, 2020 to Oct 06, 2020
Total Number of Hours
10.0
Course Fee(s)
Standard Price $1,380.00
Type
2 week
Dates
Dec 02, 2020 to Dec 15, 2020
Total Number of Hours
10.0
Course Fee(s)
Standard Price $1,380.00
Type
2 week
Dates
Feb 10, 2021 to Feb 23, 2021
Total Number of Hours
10.0
Course Fee(s)
Standard Price $769.00
Type
2 week
Dates
Apr 21, 2021 to May 04, 2021
Total Number of Hours
10.0
Course Fee(s)
Standard Price $769.00
Type
2 week
Dates
Jun 30, 2021 to Jul 13, 2021
Total Number of Hours
10.0
Course Fee(s)
Standard Price $769.00
Type
2 week
Dates
Sep 08, 2021 to Sep 21, 2021
Total Number of Hours
10.0
Course Fee(s)
Standard Price $769.00
Type
2 week
Dates
Nov 17, 2021 to Nov 30, 2021
Total Number of Hours
10.0
Course Fee(s)
Standard Price $769.00
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