Forecasting daily meteorological time series using ARIMA and regression models
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| Authors: | M. Murat1, I. Malinowska1, M. Gos2, J. Krzyszczak2 1Department of Mathematics, Lublin University of Technology, Nadbystrzycka 38a, 20-618 Lublin, Poland 2Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland |
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| Abstract : | The daily air temperature and precipitation time series recorded between January 1, 1980 and December 31, 2010 in four European sites (Jokioinen, Dikopshof, Lleida and Lublin) from different climatic zones were modeled and forecasted. In our forecasting we used the methods of the Box-Jenkins and Holt-Winters seasonal auto regressive integrated moving-average, the autoregressive integrated moving-average with external regressors in the form of Fourier terms and the time series regression, including trend and seasonality components methodology with R software. It was demonstrated that obtained models are able to capture the dynamics of the time series data and to produce sensible forecasts. |
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| Keywords : | regression models, forecast, time series, meteorological quantities | ||||||||||
| Language : | English | ||||||||||