Accurately identifying lag structures between related time series is essential in public health forecasting, particularly during epidemics where delays between infections and hospitalizations affect planning. Using a simulated SEIR model and SAS Viya’s PROC TSSELECTLAG, distance correlation is shown to outperform Pearson correlation by correctly identifying nonlinear lag relationships—such as the true seven-day lag between new infections and hospital admissions.

SAS Blog

31.05.2025

Automatic Linting of SAS Code with SASjs - The SAS Data Science Blog

Demonstrate how to use the SASjs library in conjunction with GitHub actions to ensure consistent SAS code quality throughout a repository

SAS Blog

14.06.2025

Optimize spare parts inventory under uncertainty with SAS: A simulation-based approach - The SAS Data Science Blog

This post explores how to optimize spare parts inventory under uncertainty. We use a simulation-based approach with the SAS DATA step.

SAS Blog

26.06.2025

Your questions answered: How to succeed in generative AI using LLMOps and agents - The SAS Data Science Blog

This guide explains how businesses can successfully implement generative AI by focusing on narrow use cases, curating data, leveraging AI agents, safeguarding sensitive information, monitoring for bias and toxicity, and ensuring model accuracy and relevancy.

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Detecting lags in nonlinear time series with PROC TSSELECTLAG in SAS Viya - The SAS Data Science Blog

Automatic Linting of SAS Code with SASjs - The SAS Data Science Blog

Optimize spare parts inventory under uncertainty with SAS: A simulation-based approach - The SAS Data Science Blog

Your questions answered: How to succeed in generative AI using LLMOps and agents - The SAS Data Science Blog

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