SYEN 4352 - Spatial Time Series

Three hours lecture. Three credit hours.

Instead of a single stream of data, multiple streams are gathered over the target can provide better information. Because of the inherent spatial correlation among these data streams, spatial time-series can play an important role in multiple-sensor and other data-intensive applications. Image-processing applications include image rectification and restoration, image enhancement, image classification, and data merging. Signal processing applications include Spatial-temporal Autoregressive Moving-Average model and Intervention Analysis. Unifying these diverse analyses and applications is Markov Random Field Theory. Dual listed in the Graduate Catalog as SYEN 5352.

Prerequisites: SYEN 3312, SYEN 3314 or STAT 3353, and consent of instructor.