Statistical Modeling and Prediction of Rainfall Time Series Data.

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dc.contributor.authorOlatayo, T. O.
dc.contributor.authorTaiwo, A. I.
dc.contributor.authorAfolayan, Razaq
dc.date.accessioned2021-10-12T11:40:37Z
dc.date.available2021-10-12T11:40:37Z
dc.date.issued2014-07
dc.description.abstractClimate and rainfall are highly non-linear and complicated phenomena, which requires classical , modern and detailed models to obtain accurate prediction. In this paper, we present tools for modelling and predicting the behavioural pattern in rainfall phenomena based on past observations. The paper introduces three fundamentally different approaches for designing a model , the statistical method based on autoregressive Integrated Moving Average(ARIMA), the emerging fuzzy time series(FTS) model and non-parametric method(Theil's regression). In order to evaluate prediction efficiency, we made use of 31 years annual rainfall data from year 1982 to 2012 of Ibadan, Oyo state, Nigeria. The Fuzzy time series model has its universe of discourse divided into 13 intervals and the interval with the largest number of rainfall data is divided into 4 sub intervals of equal length. Three rules were used to determine if the forecast value under FTS is upward 0.75 -point, middle or downward 0.25-point. ARIMA(1,2,1) was used to derive the weights and the regression coefficients, while the Theil's regression was used to fit a linear model. The performance of the model was evaluated using mean squared forecast error(MAE) and root mean square forecast error (RMSE) and coefficient of determination. The study reveals that FTS model can be used as an appropriate forecasting tool to predict the rainfall, since it outperformed the ARIMA and Theil's models.en_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/6591
dc.language.isoenen_US
dc.publisherJournal of the Nigerian Association of Mathematical Physicsen_US
dc.relation.ispartofseriesVol. 27;201-208
dc.subjectFuzzy time seriesen_US
dc.subjectAutoregressive integrated moving averageen_US
dc.subjectTheil's regressionen_US
dc.subjectRoot mean square forecast erroren_US
dc.subjectMean squared forecast erroren_US
dc.subjectCoefficient of determinationen_US
dc.titleStatistical Modeling and Prediction of Rainfall Time Series Data.en_US
dc.typeArticleen_US

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