Degree Name
MS (Master of Science)
Program
Mathematical Sciences
Date of Award
5-2024
Committee Chair or Co-Chairs
Jeff Knisley
Committee Members
Mostafa Zahed, Robert Price
Abstract
Time series analysis is a statistical technique used to analyze sequential data points collected or recorded over time. While traditional models such as autoregressive models and moving average models have performed sufficiently for time series analysis, the advent of artificial neural networks has provided models that have suggested improved performance. In this research, we provide a custom neural network; a shift encoder that can capture the intricate temporal patterns of time series data. We then compare the sparse matrix of the shift encoder to the parameters of the autoregressive model and observe the similarities. We further explore how we can replace the state matrix in a state-space model with the sparse matrix of the shift encoder.
Document Type
Thesis - unrestricted
Recommended Citation
Donkoh, Patrick, "Interpreting Shift Encoders as State Space models for Stationary Time Series" (2024). Electronic Theses and Dissertations. Paper 4366. https://dc.etsu.edu/etd/4366
Copyright
Copyright by the authors.