The SAS Data Science Blog
Advanced analytics from SAS data scientists
Panel data are commonly used in today’s economics research. Panel data regression stands out as a powerful tool that aids in unraveling trends and patterns that evolve over time. This tool is particularly valuable when considering hidden factors in the investigations of cause-and-effect relationships. In this post, you will be
Learn how the %FiniteHMM macro can automatically pre-process input data as well as post-process output tables for finite Hidden Markov Models (HMMs) using PROC HMM.
SAS Model Manager and the sasctl packages aim to create a seamless ModelOps and MLOps process for Python and R models. Python and R models are not second-class citizens within SAS Model Manager. SAS, Python, and R models can be easily managed using our no-code/low-code interface. This is an interface that can be extended to support a variety of use cases.
A close look at the first of five DevOps ideals in Gene Kim's book, “The Unicorn Project."
SAS' Phuong Ngo demonstrates an automated shift-left CI/CD security workflow.
Empirical Mode Decomposition (EMD) is a powerful time-frequency analysis technique that allows for the decomposition of a non-stationary and non-linear signal into a series of intrinsic mode functions (IMFs). The method was first introduced by Huang et al. in 1998 and has since been widely used in various fields, such as signal processing, image analysis, and biomedical engineering.
