SYEN 5352 - Spatial Time Series

Three hours lecture. Three credit hours.

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

Prerequisites: SYEN 3312 or equivalent, STAT 3353 or equivalent, or Consent of Instructor.