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The development of temperature forecasting models for the state of Kerala using Seasonal Autoregressive Integrated Moving Average (SARIMA) method is presented in this article. Mean maximum and mean minimum monthly temperature data, for a period of 47 years, from seven stations, are studied and applied to develop the model. It is expected that the time-series datasets of temperature to display seasonality (and hence non-stationary), and a possible trend (due to the fact that the data spans 5 decades). Hence, the key step in the development of the models is the determination of the non-stationarity of the temperature time-series, and the transformation of the non-stationary time-series into a stationary time-series. This is carried out using the Seasonal and Trend decomposition using Loess technique and Kwiatkowski–Phillips–Schmidt–Shin test. Before carrying out this process, several preliminary tests are conducted for (1) fnding and flling the missing values, (2) studying the characteristics of the data, and (3) investigating the presence of the trend and seasonality. The non-stationary temperature time-series are transformed to stationary temperature time-series, by one seasonal diferencing and one frstorder diferencing. This information, along with the original time-series, is further utilized to develop the models using the SARIMA method. The parsimonious and best-ft SARIMA models are developed for each of the fourteen variables. The study revealed that SARIMA(2, 1, 1)(1, 1, 1)12 as the ideal forecasting model for eight out of the fourteen time-series datasets.
Wydawca
Czasopismo
Rocznik
Tom
Strony
1161--1174
Opis fizyczny
Bibliogr. 25 poz.
Twórcy
autor
- Department of Civil Engineering, NIT Calicut, Calicut 673601, India
autor
- Department of Civil Engineering, NIT Calicut, Calicut 673601, India
autor
- Department of Civil Engineering, NIT Calicut, Calicut 673601, India
Bibliografia
- 1. Aguado-Rodríguez GJ, Quevedo-Nolasco A, Castro-Popoca M, Arteaga-Ramírez R, Vázquez-Peña MA, Zamora-Morales BP (2016) Predicción de variables meteorológicas por medio de modelos arima. Agrociencia 50(1):1–13
- 2. Allen MR, Ingram WJ (2002) Constraints on future changes in climate and the hydrologic cycle. Nature 419(6903):228–232
- 3. Andronova NG, Schlesinger ME (2000) Causes of global temperature changes during the 19th and 20th centuries. Geophys Res Lett 27(14):2137–2140
- 4. Arjun KM (2013) Indian agriculture-status, importance and role in Indian economy. Int J Agric Food Sci Technol 4(4):343–346
- 5. Cleveland RB et al (1990) Stl: a seasonal-trend decomposition procedure based on loess. citeulike-article-id:1435502
- 6. Gilbert RO (1987) Statistical methods for environmental pollution monitoring. Wiley, New York
- 7. Gocic M, Trajkovic S (2013) Analysis of changes in meteorological variables using Mann–Kendall and Sen’s slope estimator statistical tests in Serbia. Glob Planet Change 100:172–182
- 8. Hänsel S, Medeiros DM, Matschullat J, Petta RA, de Mendonça Silva I (2016) Assessing homogeneity and climate variability of temperature and precipitation series in the capitals of North-Eastern Brazil. Front Earth Sci 4:29
- 9. Hyndman RJ, Athanasopoulos G (2018) Forecasting: principles and practice. OTexts, Melbourne
- 10. Indian Network for Climate Change Assessment and India Ministry of Environment (2010) Climate Change and India: a 4 × 4 assessment, a sectoral and regional analysis for 2030s, vol 2. Ministry of Environment & Forests, Government of India, New Delhi
- 11. Jain SK, Kumar V (2012) Trend analysis of rainfall and temperature data for India. Curr Sci 102:37–49
- 12. Kang H (2013) The prevention and handling of the missing data. Korean J Anesthesiol 64(5):402
- 13. Kendall M (1975) Rank correlation methods. Charles Griffin, London (There is no corresponding record for this reference)
- 14. Kocsis T, Kovács-Székely I, Anda A (2017) Comparison of parametric and non-parametric time-series analysis methods on a long-term meteorological data set. Cent Eur Geol 60(3):316–332
- 15. Kocsis T, Kovács-Székely I, Anda A (2020) Homogeneity tests and non-parametric analyses of tendencies in precipitation time series in Keszthely, Western Hungary. Theor Appl Climatol 139(3–4):849–859
- 16. Kwiatkowski D, Phillips PC, Schmidt P, Shin Y et al (1992) Testing the null hypothesis of stationarity against the alternative of a unit root. J Econom 54(1–3):159–178
- 17. Lai Y, Dzombak DA (2020) Use of the autoregressive integrated moving average (ARIMA) model to forecast near-term regional temperature and precipitation. Weather Forecast 35:959–976
- 18. Mann HB (1945) Nonparametric tests against trend. Econom J Econom Soc 13:245–259
- 19. Mills TC (2014) Time series modelling of temperatures: an example from k efalonia. Meteorol Appl 21(3):578–584
- 20. Radziejewski M, Kundzewicz ZW (2004) Detectability of changes in hydrological records/possibilité de détecter les changements dans les chroniques hydrologiques. Hydrol Sci J 49(1):39–51
- 21. Sen PK (1968) Estimates of the regression coefficient based on Kendall’s tau. J Am Stat Assoc 63(324):1379–1389
- 22. Tiwari P, Kar S, Mohanty U, Dey S, Kumari S, Sinha P (2016) Seasonal prediction skill of winter temperature over North India. Theor Appl Climatol 124(1–2):15–29
- 23. Trenberth KE (1999) Conceptual framework for changes of extremes of the hydrological cycle with climate change. In: Weather and climate extremes. Springer, Dordrecht, pp 327–339
- 24. Wang H, Huang J, Zhou H, Zhao L, Yuan Y (2019) An integrated variational mode decomposition and arima model to forecast air temperature. Sustainability 11(15):4018
- 25. Wanishsakpong W, Owusu BE (2020) Optimal time series model for forecasting monthly temperature in the southwestern region of Thailand. Model Earth Syst Environ 6(1):525–532
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-09d30613-162b-4c9c-8319-44e67a1b2477