Date of Graduation
2014
Document Type
Thesis
Degree Type
MS
College
Statler College of Engineering and Mineral Resources
Department
Industrial and Managements Systems Engineering
Committee Chair
Rashpal S Ahluwalia
Committee Co-Chair
Robert C Creese
Committee Member
Feng Yang
Abstract
This research utilized five economic factors; 1) Consumer Price Index, 2) Return on Treasury Securities, 3) Total Nonfarm payroll, 4) Jobless Claims Filed, and 5) Stand & Poor 500 index to predict US unemployment rate. Historical time series data was obtained from the Economic Research web site of the Federal Reserve Bank of St. Louis and other finance web site.;Multiple Linear Regression, Back Propagation Algorithm, and Support Vector Regression techniques were utilized to predict US unemployment rate. Based on Mean Squared Error and adjusted R2 values, the Support Vector Regression technique provided superior results for the given dataset. Future US unemployment rate was predicted with an average absolute error value of 0.815, 0.13 and 0.07 using MLR, ANN and SVR, respectively.
Recommended Citation
Ansari, Azadeh, "Application of Neural Network-Support Vector Technique to Forecast U.S. Unemployment Rate" (2014). Graduate Theses, Dissertations, and Problem Reports. 5112.
https://researchrepository.wvu.edu/etd/5112