Online Workshop: Business Forecasting Techniques, Best Practices & Application Using Forecast Pro

Introduction to Forecasting
A broad overview of business forecasting and its various uses within the organization. Topics include approaches to forecasting, features of data, the role of judgment, selection of appropriate forecasting methods for varied data sets and resources for forecasters.

Components of Data
An in-depth look at the different components found in time series data including trends, seasonal patterns, business cycles, trading-day variations, interventions (events) and noise. Discussion includes the forms the components can take, spotting local vs. global components, interpretation of business cycle indicators and the use of decomposition routines.

Exponential Smoothing
A survey of exponential smoothing techniques with particular emphasis on the Holt-Winters family of models. Topics include the pros and cons of using these models, when they are best used, how they work, identifying model components, parameter optimization and model diagnosis.

Extensions to Exponential Smoothing
This session examines three useful extensions to the exponential smoothing model family. The first is the NA-CL model which will often improve forecast accuracy for data sets that exhibit a “selling season” whereby the majority of the demand occurs at specific times of the year (e.g., snow shovels, flu vaccines, etc.). The second is the Croston’s Intermittent Demand Model which is used to forecast data that exhibit frequent zero demand periods. The third is the Custom Component Model which allows some of the components to be estimated from the data and others to be customized by the forecaster.

Box-Jenkins (ARIMA) Models:
An exploration into the use of ARIMA models for business forecasting. Topics include the advantages/disadvantages of using these models, how and when they should be applied, automatic identification procedures and model diagnostics.

Forecast Accuracy and Evaluation
A detailed look at evaluating the accuracy of forecasting methods. Topics include the distinction between within-sample and out-of-sample errors, a survey of error measurement statistics, a summary of findings from forecasting competitions, and an explanation of how to use both real-time tracking reports and simulations as predictors of model performance.

Identifying Problems in Your Forecasting Process
Approaches for focusing on critical items when forecasting large volumes of data. Topics include evaluating and forecasting SKU data, filtering and ABC (Pareto) classification, outlier detection and correction, exception reporting and measuring accuracy across multiple time series.

Event-Index Models
Event-index models extend the functionality of exponential smoothing models by providing adjustments for promotions, strikes and other non-calendar-based events. This session addresses how these models work, how and when they should be used, and how to customize their design to best suit your needs.

Multiple-Level Forecasting
This session explores hierarchical forecasting techniques. Topics include the need for forecasting at various levels, product vs. geographical hierarchies, reconciliation strategies, top-down vs. bottom-up approaches, the use of proportional allocation and adjustment for seasonality.

New Product Forecasting
This session explores various approaches for forecasting new products. Topics include the pros and cons of different methods based on a product’s classification and a review of popular methods including item supersession, forecast by analogy and the Bass diffusion model.

Forecasting with Machine Learning
This session overviews the basics and benefits of forecasting with machine learning (ML). Topics include the basics of machine learning powered forecasting, when ML is likely to improve your forecasts, how to use the completely automatic ML option in Forecast Pro and how to build custom ML models in Forecast Pro.

Dynamic Regression
A detailed look into the ins and outs of regression fore­casting. Topics include when regression models are best applied, how to build the models, ordinary least squares, leading indicators, lagged variables, Cochrane-Orcutt models, hypothesis testing and the use of “dummy” vari­ables.