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Modern signal processing heavily relies on probability and statistics. Naturally, these subjects are emphasized in the signal processing curricula at both the undergraduate and graduate levels. Historically, however, both probability and statistics were developed to solve various applied problems arising in economics and finance. Specifically, probability theory owes its existence to games of chance, and statistics made its first significant appearance in the pricing of insurance premia and bonds. More recently, both finance and economics have been steady sources of data—much of it one-dimensional temporal signals—generating demand for the statistical analysis and modeling tools commonly developed and used by signal processing researchers and practitioners.
Such interrelationships suggest that courses on applied probability and random processes taught to signal processing students should frequently turn to finance for motivating examples, illustrations, homework problems, and lab exercises. In addition, courses dedicated to financial applications of signal processing should perhaps be as common as those on applications to biomedical imaging and speech analysis.
How to incorporate financial signal processing into the undergraduate and graduate curricula? You can find suggestions in the column article “Incorporating Financial Applications in Signal Processing Curricula” in the September issue of IEEE Signal Processing Magazine. In this column article, Dr. Ilya Pollak shared several ways in which financial signal processing has been incorporated into the undergraduate and graduate curricula at Purdue’s School of Electrical and Computer Engineering (ECE) and encouraged other schools to do the same.
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