Financial time series forecasting using twin support vector regression.

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ID: 70107
2019
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Abstract
Financial time series forecasting is a crucial measure for improving and making more robust financial decisions throughout the world. Noisy data and non-stationarity information are the two key factors in financial time series prediction. This paper proposes twin support vector regression for financial time series prediction to deal with noisy data and nonstationary information. Various interesting financial time series datasets across a wide range of industries, such as information technology, the stock market, the banking sector, and the oil and petroleum sector, are used for numerical experiments. Further, to test the accuracy of the prediction of the time series, the root mean squared error and the standard deviation are computed, which clearly indicate the usefulness and applicability of the proposed method. The twin support vector regression is computationally faster than other standard support vector regression on the given 44 datasets.
Reference Key
gupta2019financialplos Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gupta, Deepak;Pratama, Mahardhika;Ma, Zhenyuan;Li, Jun;Prasad, Mukesh;
Journal PloS one
Year 2019
DOI
10.1371/journal.pone.0211402
URL
Keywords
time series modeling animal disease disease control strategy foot and mouth disease indirect costs market impact vector error correction model

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