From time series analysis to a modified ordinary differential equation. Issue 2 (June 2018)

• Record Type: Journal Article
• Authors: Xue, Meiyu; Lai, Choi-Hong
• Is Part Of: Journal of algorithms & computational technology. Volume 12:Issue 2(2018)
• Page Start: 85
• Page End: 90
• Publication Date: 2018-06
• Subjects: Time series analysis -- autoregressive integrated moving average -- ordinary differential equation -- mean absolute percentage error; Computer algorithms -- Periodicals; Numerical calculations -- Periodicals; Computer algorithms; Numerical calculations; Periodicals; 518.1
• DOI: 10.1177/1748301817751480
• Languages: English
• ISSNs: 1748-3018

Abstract

In understanding Big Data, people are interested to obtain the trend and dynamics of a given set of temporal data, which in turn can be used to predict possible futures. This paper examines a time series analysis method and an ordinary differential equation approach in modeling the price movements of petroleum price and of three different bank stock prices over a time frame of three years. Computational tests consist of a range of data fitting models in order to understand the advantages and disadvantages of these two approaches. A modified ordinary differential equation model, with different forms of polynomials and periodic functions, is proposed. Numerical tests demonstrated the advantage of the modified ordinary differential equation approach. Computational properties of the modified ordinary differential equation are studied.