Research Reports from the Department of Operations
Document Type
Thesis
Publication Date
5-1-1984
Abstract
A forecasting methodology has been developed which is based on information theory. This methodology uses the idea that autoregressive (AR) representation of a stationary, Normally distributed time series is equivalent to a model of such a time series which has maximum information content. To apply this idea, two programs were developed. The first program utilizes an Adaptive Sequential Segmentation Algorithm, which is based on an information theoretic measure, to detect subseries in a piecewise stationary time series. This is done to accommodate abrupt or discontinuous changes in the underlying process parameters. A second program utilizes the Akaike Information Criterion to determine the optimal degree of differencing and optimal AR order of an AR model fitted to a given series or subseries. Forecasts and the frequency spectrum of this model are computed. Performance tests of this methodology, and a comparison with the Box-Jenkins methodology is included.
Keywords
Operations research, Time-series analysis, Forecasting--Statistical methods, Information theory, Autoregression (Statistics), Stochastic processes, Algorithms, Akaike information criterion
Publication Title
Master's thesis/Technical Memorandums from the Department of Operations, School of Management, Case Western Reserve University
Issue
Technical memorandum no. 534 ; Submitted in partial fulfillment of the requirements for the Degree of Master of Science.
Rights
This work is in the public domain and may be freely downloaded for personal or academic use
Recommended Citation
McLain, Keith Kerwin, "An Information Theoretic Forecasting Methodology" (1984). Research Reports from the Department of Operations. 247.
https://commons.case.edu/wsom-ops-reports/247