Forecasting daily meteorological time series using ARIMA and regression models

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.

Statistical Modelling of Maximum and Minimum Temperature in Thiruvananthapuram District

“…A study by Unnikrishnan et al(2018) on forecasting weather parameters showed that ARIMA (011) (011) is the most commonly fitted ARIMA model for seasonal parameters. Similar study by Murat et al (2018) on forecasting meteorological time series data found that ARIMA models is best fit for air temperature studies. In the study by El-Mallah and Elsharkawy (2016) on time series modelling and short term forecast of yearly temperature disclosed that the quadratic ARIMA model and linear ARIMA model had the best overall performance in making short-term forecasts of yearly total temperature in Libya.…”

Time Decomposition and Short-Term Forecasting of Hydrometeorological Conditions in the South Baltic Coastal Zone of Poland

“…The seasonal ARIMA (p,d,q) (P,D,Q) m process, also referred to as SARIMA (p,d,q) (P,D,Q) m is given by [7,35]:…”

“…Its main aim is to carefully and rigorously study the past observations of a time-series to develop an appropriate model which can predict future values for the series. It has three control constants (trend, seasonal, and irregular influence), which can control and manage influence of time segmentation through the specific time duration [7]. The ARIMA models are now widely used for various applications, such as natural environment [8][9][10][11], medicine [12], and engineering [13,14].…”

“…In recent years, one of the most popular ways of time-series modelling is ARIMA model main aim is to carefully and rigorously study the past observations of a time-series to dev appropriate model which can predict future values for the series. It has three control constants seasonal, and irregular influence), which can control and manage influence of time segme through the specific time duration [7]. The ARIMA models are now widely used for applications, such as natural environment [8][9][10][11], medicine [12], and engineering Autoregressive integrated moving average modelling is often used in many areas of the ec e.g., in forecasting tourist traffic at airports [15], in road transport [16,17], in real estate price a [18], unemployment analysis [19], in studies of electricity consumption and their price fo [20,21], and in wind energy and runoff forecasts [22,23].…”

Mineral nitrogen content in hydrographic areas of Poland depending on land use

“…The content of mineral nitrogen in the soil is also impacted by the biological activity of the soil, which in turn may be influenced by soil moisture (Wnuk et al, 2017) or N fertilization (Walkiewicz et al, 2018), and by atmospheric conditions. For this reason, research concerning the spatio-temporal variability of meteorological series from different climate zones Hoffmann et al, 2017;Krzyszczak et al, 2017a;2017b;2019) and their prediction (Murat et al, 2018) is extremely important. It allows for the assessment of the likely impact of climate change not only on agricultural production, but also on the content of macroelements (NPK) in the soil.…”