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Forecasting Analytics

Seminar #73104 OnDemand

Choose an appropriate time series forecasting method, fit the model, and evaluate its performance

Zero in on the most popular business forecasting models and how to use them: Regression models, smoothing methods including moving average (MA) and exponential smoothing, and autoregressive models (AR). You will also discuss enhancements such as second-layer models and ensembles.

You will begin by characterizing time series and the forecasting goal and evaluating predictive accuracy and data partitioning.  Next you will cover regression-based models, smoothing-based methods, and forecasting implementation issues.

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Schedule

  • [4] weeks - $589

Credits

5 CEU

AMA PDF Course Packet
The PDF Course Packet includes Curriculum and other information that can help convince your manager of the value.

Schedule

We have 1 scheduled sessions located nationwide starting between 9/12/2014 - 9/12/2014

Date Location Duration Attendees  
Sep 12, 2014 - Oct 9, 2014 OnDemand 4 Weeks
Registering more than 4  people, please call 1-877-566-9441.

How You Will Benefit

  • Set up models to predict future events in a time series (e.g. monthly sales) 
  • Select appropriate model types (regression, smoothing) for given situations 
  • Use data partitioning and holdout samples to evaluate model performance 
  • Use Excel-based software to implement and interpret models

Learning Plan

Week 1: Characterizing Time Series and the Forecasting Goal; Evaluating Predictive Accuracy and Data Partitioning 
  • Visualizing time series 
  • Time series components 
  • Forecasting vs. explanation 
  • Performance evaluation 
  • Naive forecasts

Week 2: Regression-Based Models 

  • Overview of forecasting methods 
  • Capturing trend seasonality and irregular patterns with linear regression 
  • Measuring and interpreting autocorrelation 
  • Evaluating predictability and the Random Walk 
  • Second-layer models using Autoregressive (AR) models

Week 3: Smoothing-Based Methods 

  • Model-driven vs. data-driven methods 
  • Centered and training Moving Average (MA) 
  • Exponential Smoothing (simple, double, triple) 
  • De-trending and seasonal adjustment 
  • Differencing 

Week 4: Forecasting in Practice 

  • Forecasting implementation issues (automation, managerial forecast adjustments, and more) 
  • Communicating forecasts to stakeholders 
  • Overview of further forecasting methods (neural nets, ARIMA, and logistic regression) 
  • Forecasting binary outcomes

Who Should Attend This Course

Data scientists, data analysts, sales forecasters, marketing managers, accountants, economists, financial analysts, risk managers, anyone who needs to produce, interpret, or assess forecasts will find this course useful. Participants should be familiar with basic statistics, including linear regression.
 

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