| Records |
Author  |
Murat, M.; Malinowska, I.; Gos, M.; Krzyszczak, J. |
| Title |
Forecasting daily meteorological time series using ARIMA and regression models |
Type |
Journal Article |
| Year |
2018 |
Publication |
International Agrophysics |
Abbreviated Journal |
Int. Agrophys. |
| Volume |
32 |
Issue |
2 |
Pages |
253-264 |
| Keywords |
regression models; forecast; time series; meteorological quantities; Response Surfaces; Extreme Heat; Wheat; Climate |
| 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. |
| Address |
2018-06-14 |
| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0236-8722 |
ISBN |
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Medium |
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| Area |
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Expedition |
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Conference |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5202 |
| Permanent link to this record |
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| Author |
Murat, M.; Malinowska, I.; Hoffmann, H.; Baranowski, P. |
| Title |
Statistical modelling of agrometeorological time series by exponential smoothing |
Type |
Journal Article |
| Year |
2016 |
Publication |
International Agrophysics |
Abbreviated Journal |
International Agrophysics |
| Volume |
30 |
Issue |
1 |
Pages |
57-65 |
| Keywords |
exponential smoothing; meteorological time series; statistical forecasting; daily temperature records; weighted moving averages; climate-change; prediction; forecasts; state; weather |
| Abstract |
Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, longterm meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error. |
| Address |
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| Corporate Author |
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Thesis |
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| Publisher |
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Place of Publication |
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Editor |
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| Language |
English |
Summary Language |
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Original Title |
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| Series Editor |
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Series Title |
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Abbreviated Series Title |
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| Series Volume |
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Series Issue |
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Edition |
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| ISSN |
0236-8722 |
ISBN |
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Medium |
Article |
| Area |
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Expedition |
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Conference |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4728 |
| Permanent link to this record |
