Bayesian Inference to Time Series Data Mining

Authors

  • D. Venkatesan Department of Statistics, Annamalai University, Annamalai Nagar, India
  • R. Karnaboopathy Department of Statistics, Annamalai University, Annamalai Nagar, India

DOI:

https://doi.org/10.37591/joadms.v1i3.212

Abstract

The time series data mining (TSDM) framework is a fundamental contribution to the field of time series analysis and data mining in the recent past. Methods based on the TSDM framework are able to successfully characterize and predict complex, nonperiodic, irregular and chaotic time series. The TSDM methods overcome limitations including stationarity and linearity requirements of traditional time series analysis techniques by adopting data mining concepts for analyzing time series. This paper discusses the Bayesian Inference to data mining in time series through a case study on the IBM stock prices data.

Keywords: Time series model, autoregressive model, TSDM, variance change,
posterior distribution.

 

References

Karnaboopathy R., Venkatesan D. Data

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Bowerman B.L., O’Connell R.T.

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approach. 3rd Edn. Belmont, California,

USA: Duxbury Press; 1993.

Hsu D.A. Tests for variance shift at an

unknown time point. J Roy Statistic

Soci-C. 1977; 26: 279–284p.

Box G.E.P., Jenkins G.M. Time series

analysis, forecasting and control. San

Francisco, USA: Holden Day; 1970

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Published

2014-12-02

Issue

Vol. 1 No. 3 (2014)

Section

Research Articles

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