Abstract
An approach to discovering rules in nonstationary k-valued Multidimensional time series is proposed. It allows one to discover rules that are subject to “smooth” structural changes with the course of time. A measure of rule similarity is proposed to describe such changes, and its application in the form of weight in the graph of rules is discussed. The discovered rules can be used to predict the next elements in the multidimensional time series, to analyze the phenomenon described by this multidimensional time series, and to model it. This allows one to use the proposed algorithm for predicting time series and for examining and describing the processes that can be represented by a multidimensional time series. Means for the direct practical application of the proposed methods of the analysis and prediction of time series are described, and the use of those methods for the short-range prediction of a real-life multidimensional time series consisting of the stock prices of companies operating in similar fields is discussed.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Computational Mathematics and Mathematical Physics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
