DYNAMICLINEAR Procedure
Overview: DYNAMICLINEAR Procedure
Dynamic linear models (DLMs) are a cornerstone in the analysis of time series data. Their versatility has led to their widespread application in diverse fields such as economics, finance, science, and engineering. One of the primary SAS tools for working with DLMs is the DYNAMICLINEAR procedure, which focuses on simultaneous graphical dynamic linear models (SGDLMs) in particular.
PROC DYNAMICLINEAR offers a number of features, including the following:
the ability to specify the model structure, enabling users to define the relationship between variables
a provision for setting initial values for prior distributions, which is pivotal in Bayesian time series analysis
detailed output options, which include filtering results. These help you refine and adjusting the model for better predictions.
comprehensive forecasting tools, which deliver the mean, standard error, covariance matrix, median, and confidence intervals for forecasts of variables. Such in-depth statistics provide a holistic view of potential future scenarios.
support for multistep forecasts after each observation, which enhance the granularity and flexibility of prediction
Because PROC DYNAMICLINEAR runs on SAS Cloud Analytic Services (CAS), it also does the following:
enables you to run on a cluster of machines that distribute the data and the computations
exploits all the available cores and concurrent threads