The SAS Data Science Blog
Advanced analytics from SAS data scientists
The Empirical Mode Decomposition for handling non-stationary time series
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.
Poisson HMM: The model of count time series
SAS' Ji Shen introduces you to an effective solution for modeling and forecasting count time series.
Using SAS Viya Machine Learning to classify COVID from non-COVID
In her first blog post, SAS' Mu He shows you how to train a convolutional neural network that can accurately detect patients with COVID-19 using the transfer learning technique.