Date of Graduation
12-2015
Document Type
Thesis
Degree Name
Master of Science in Computer Science (MS)
Degree Level
Graduate
Department
Computer Science & Computer Engineering
Advisor/Mentor
Michael Gashler
Committee Member
Wing Ning Li
Second Committee Member
Xintao Wu
Keywords
Applied sciences
Abstract
We present a neural network technique for the analysis and extrapolation of time-series data called Neural Decomposition (ND). Units with a sinusoidal activation function are used to perform a Fourier-like decomposition of training samples into a sum of sinusoids, augmented by units with nonperiodic activation functions to capture linear trends and other nonperiodic components. We show how careful weight initialization can be combined with regularization to form a simple model that generalizes well. Our method generalizes effectively on the Mackey-Glass series, a dataset of unemployment rates as reported by the U.S. Department of Labor Statistics, a time-series of monthly international airline passengers, the monthly ozone concentration in downtown Los Angeles, and an unevenly sampled time-series of oxygen isotope measurements from a cave in north India. We find that ND outperforms popular time-series forecasting techniques including ARIMA, SARIMA, SVR with a radial basis function, Gashler and Ashmore’s model, and echo state networks.
Citation
Godfrey, L. (2015). Neural Decomposition of Time-Series Data for Effective Generalization. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/1360