Enhance your capabilities with SAS Visual Forecasting and advanced data forecasting software.
Suggested by: Coursera (What is Coursera?)
No prior knowledge required
No unnecessary risks
Using SAS Visual Forecasting and other SAS tools, you will learn to explore time series, create and select features, build and manage a large-scale forecasting system, and use a variety of models to identify, evaluate, and predict important signal components.
In this internship project, students will discover signal components in high-value series and then specify custom specifications that fit these series. These custom specifications are integrated into a large-scale forecasting system that students have created to automate the process of model creation, model selection, and forecasting.
Students adjust for recurring events and anomalies in the data generation process to improve the automated prediction system.
This course focuses on data exploration, feature generation, and feature removal for time series. Topics covered include:
In this course, you will learn to perform motif analysis and apply spectral or frequency domain analysis. You will also discover how distance measures work, implement applications, explore signal components, and create features for time series.
This course is suitable for analysts with a quantitative background as well as experts in the field who want to add tools to their time series toolbox. Before starting the course, you should be comfortable with basic statistical concepts. You can gain this experience by completing the Statistics with SAS course. Familiarity with matrices and principal component analysis may also be helpful, but is not required.
In this course, you will learn to develop and maintain a large-scale forecasting project using SAS Visual Forecasting tools. The initial emphasis will be on:
This course is suitable for analysts who want to improve their learning skills with analytical tools suitable for examining, transforming, modeling, predicting, and managing data that contains variables collected over time. Furthermore, the course is mainly based on syntax, so analysts taking this course should have basic knowledge of coding. Experience with an object-oriented programming language is beneficial, as is familiarity with working with large tables.
In this course, you will learn to build, refine, extend, and in some cases interpret models designed for a single set of sequences. There are three modeling approaches presented:
The course concludes by considering how forecast accuracy can be improved by combining the strengths of the different approaches. The final lesson includes demonstrations on creating combined (or ensemble) forecasts and hybrid models.
This course is suitable for analysts who want to improve their learning skills with analytical tools suitable for examining, transforming, modeling, predicting, and managing data that contains variables collected over time.
This course uses a variety of different software tools. Familiarity with Base SAS, SAS/ETS, SAS/STAT, and SAS Visual Forecasting, as well as open source tools for handling structured data and modeling, is helpful but not required. The lessons on Bayesian analysis and machine learning models assume prior knowledge of these topics. One way students can gain this background is by completing the SAS courses: Bayesian Analysis with SAS and Machine Learning with SAS Viya.
