• Title/Summary/Keyword: Arima (AR(1)) Method

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The Study for Software Future Forecasting Failure Time Using ARIMA AR(1) (ARIMA AR(1) 모형을 이용한 소프트웨어 미래 고장 시간 예측에 관한 연구)

  • Kim, Hee-Cheul;Shin, Hyun-Cheul
    • Convergence Security Journal
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    • v.8 no.2
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    • pp.35-40
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    • 2008
  • Software failure time presented in the literature exhibit either constant, monotonic increasing or monotonic decreasing. For data analysis of software reliability model, data scale tools of trend analysis are developed. The methods of trend analysis are arithmetic mean test and Laplace trend test. Trend analysis only offer information of outline content. In this paper, we discuss forecasting failure time case of failure time censoring. The used software failure time data for forecasting failure time is random number of Weibull distribution(shaper parameter 1, scale parameter 0.5), Using this data, we are proposed to ARIMA(AR(1)) and simulation method for forecasting failure time. The practical ARIMA method is presented.

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GENERALISED PARAMETERS TECHNIQUE FOR IDENTIFICATION OF SEASONAL ARMA (SARMA) AND NON SEASONAL ARMA (NSARMA) MODELS

  • M. Sreenivasan;K. Sumathi
    • Journal of applied mathematics & informatics
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    • v.4 no.1
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    • pp.135-135
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    • 1997
  • Times series modeling plays an important role in the field of engineering, Statistics, Biomedicine etc. Model identification is one of crucial steps in the modeling of an AutoRegreesive Moving Average(ARMA(p, q)) process for real world problems. Many techniques have been developed in the literature (Salas et al., McLeod et al. etc.) for the identification of an ARMA(p, q) Model. In this paper, a new technique called The Generalised Parameters Technique is formulated for seasonal and non-seasonal ARMA model identification. This technique is very simple and can e applied to any given time series. Initial estimates of the AR parameters of the ARMA model are also obtained by this method. This model identification technique is validated through many theoretical and simulated examples.