This course teaches you how to create and manage a complete forecasting system using the SAS Forecast Server procedures, giving you the power to confidently plan your business operations.

Learn how to

• process time series data
• automate the forecasting of the majority of your series in a large-scale forecasting process
• handle exceptions by adding custom models and selection lists to a model repository
• create and manage event variables and event variable data sets to use as inputs to forecast models
• implement best practices in model selection, data hierarchy construction, and statistical forecast reconciliation processes.

Who should attend

Experienced data scientists and analytic leads who want to learn to develop scripts in the SAS Forecast Server programming language to build, maintain, and optimize the performance of their forecasting system

Formats available	Standard duration
Classroom:	3 days
Live Web Classroom:	6 half-day session(s)	System Requirements

Before attending this course, you should

• have experience with a Windows computing environment
• have experience using a product such as Microsoft Excel to enter or transfer data and to perform elementary analyses such as computing row and column totals and averages, and producing charts and plots
• have taken Forecasting Using SAS Forecast Server Software or have equivalent experience with time series data and modeling
• have taken SAS Programming 1: Essentials or have equivalent experience.

Ideally, students should have a bachelor's degree or equivalent experience in business, computer science, or a subject matter field that includes quantitative analysis. Forecasting experience is helpful but not required.

This course addresses SAS/ETS, SAS Forecast Server, SAS/STAT software.

Specifically, this course emphasizes the High-Performance Forecasting procedure of SAS Forecast Server.

Introduction

• motivation: the large-scale forecasting problem
• layout of the course

Exploring and Processing Timestamped Data

• accumulation: transforming transactional data into time series data
• handling missing and zero-valued intervals
• aggregation: building the data hierarchy
• two feasible data layouts for SAS Forecast Server
• using the TIMEDATA procedure
• identifying systematic variation in the data
• a SAS toolbox for exploring time series data

The Design of SAS Forecast Server-Based Forecasting Software

• the design of SAS Forecast Server-based forecasting software
• functionality overview: system inputs and automatic model selection

Diagnosing and Selecting Models Automatically

• HPFDIAGNOSE procedure: automatic model specification
• model selection lists and automatic model selection

Creating Custom Models and Managing Model Lists

• creating custom models: HPFxxxSPEC procedures
• creating and managing model selection lists: HPFSELECT procedure

Using the Events Functionality

• creating and managing event variables: HPFEVENTS procedure
• using the HPFEVENTS procedure: extensions and further details

Reconciling Statistical Forecasts

• basic forecast reconciliation using the HPFRECONCILE procedure
• disaggregation methods in forecast reconciliation

Producing, Assessing, and Modifying Forecasts

• honest assessment
• preparing to generate forecasts: accumulation and aggregation of the data
• preparing to generate forecasts: creating custom models, diagnosing models, and building a model selection list
• generating forecasts: automatic model selection and forecast outputs
• assessing system accuracy and generating reconciled forecasts

Rolling the Forecasting System Forward in Time

• forecasting by exception
• assessing model degradation
• handling data updates and structural changes