COMPARISON OF FORECAST PERFORMANCES OF DIFFERENT METHODS BY ANALYSIS OF DYNAMIC INTERACTIONS BETWEEN CLIMATE VARIABLES: NIĞDE PROVINCE EXAMPLE

Year 2025, Volume: 13 Issue: 4, 1042 - 1062, 01.12.2025

Münevver Gizem Gümüş , Hasan Çağatay Çiftçi , Kutalmış Gümüş

https://doi.org/10.36306/konjes.1689527

Abstract

The dynamic nature of climate is shaped by interactions among meteorological parameters such as humidity, temperature, wind, and precipitation. Analyzing these interactions is essential for understanding climate complexity. This study examines the dynamic relationships between meteorological variables in Niğde, Turkey, using data from 1950 to 2020, and compares the forecasting performance of various time series models. Univariate analysis was conducted using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, while multivariate analysis involved the Vector Autoregressive (VAR), Vector Error Correction Model (VECM), and Bayesian Vector Autoregressive (BVAR) models. Granger Causality Test, Johansen Cointegration Test, and Impulse-Response Function were applied to assess interactions among climate variables. The analysis showed that climate variables significantly influence one another, highlighting the importance of these interactions for accurate forecasting. Among the models, SARIMA demonstrated superior performance in univariate forecasting, consistently yielding lower root mean square error (RMSE) values compared to VAR, VECM, and BVAR models. These results offer a strong basis for predicting future trends in climate variables specific to the Niğde region. Additionally, the findings contribute to the formulation of regional development strategies and support climate impact management in sectors such as agriculture and water resources.

Keywords

BVAR , SARIMA , Forecasting Modeling , VAR , VECM

Ethical Statement

The authors declare that all ethical guidelines including authorship, citation, data reporting, and publishing original research are followed.

References

• A. Ambya, E. Russel, S. Paujiah, D. N. Pratama, W. Wamiliana, and M. Usman, "Analysis of data inflation energy and gasoline price by vector autoregressive model," Int. J. Energy Econ. Policy, vol. 12, no. 2, pp. 120–126, 2022.
• L. T. Abdullah, "Forecasting time series using vector autoregressive model," Int. J. Nonlinear Anal. Appl., vol. 13, no. 1, pp. 499–511, 2022.
• H. A. T. Nguyen, H. D. Nguyen, and T. H. Do, "An application of vector autoregressive model for analyzing the impact of weather and nearby traffic flow on the traffic volume," in Proc. 2022 RIVF Int. Conf. Comput. Commun. Technol. (RIVF), Ho Chi Minh city, Vietnam, pp. 328–333, IEEE, 2022.
• S. R. Sakib, K. N. Sara, M. T. H. Rasel, M. M. I. Asif, M. A. H. Nahid, M. S. Rahman, and A. Islam, "Time series analysis and forecasting of air quality index of Dhaka City of Bangladesh," in Proc. 2023 IEEE World AI IoT Congr. (AIIoT), Seattle, WA, USA, pp. 0063–0069, IEEE, 2023.
• A. F. Ningrum, A. Suharsono, and S. P. Rahayu, "Comparison of vector autoregressive and long short term memory for forecasting air pollution index in Jakarta," in Proc. 2022 6th Int. Conf. Inf. Technol., Inf. Syst. Electr. Eng. (ICITISEE), pp. 547–552, IEEE, 2022.
• T. M. Mawora, “ARIMA and vector autoregressive model evaluation in forecasting rainfall: A case of Kisumu”, Doctoral dissertation, Maseno Univ., Kisumu, Kenya, 2022.
• Y. O. Ouma, D. Moalahi, G. Anderson, B. Nkwae, P. Odirile, B. P. Parida, and J. Qi, "Predicting the variability of dam water levels with land-use and climatic factors using Random Forest and Vector AutoRegression models," in Remote Sens. Agric., Ecosyst., Hydrol. XXIV, vol. 12262, pp. 54–66, SPIE, 2022.
• S. Hartini, M. P. Hadi, S. Sudibyakto, and A. Poniman, "Application of vector auto regression model for rainfall-river discharge analysis," Forum Geografi, vol. 29, no. 1, pp. 118-127, Aug. 2015.
• P. S. Hou, L. M. Fadzil, S. Manickam, and M. A. Al-Shareeda, "Vector autoregression model-based forecasting of reference evapotranspiration in Malaysia," Sustainability, vol. 15, no. 4, p. 3675, 2023.
• S. Winarno, M. Usman, and D. Kurniasari, "Application of Vector Error Correction Model (VECM) and impulse response function for daily stock prices," J. Phys.: Conf. Ser., vol. 1751, no. 1, p. 012016, 2021.
• K. Karagöz and R. Keskin, "Impact of fiscal policy on the macroeconomic aggregates in Turkey: Evidence from BVAR Model," Procedia Econ. Finance, vol. 38, pp. 408–420, 2016.
• R. I. da Rocha Lima Filho, "Does PPI lead CPI in Brazil?" Int. J. Prod. Econ., vol. 214, pp. 73–79, 2019.
• K. A. Tsioptsia, E. Zafeiriou, D. Niklis, N. Sariannidis, and C. Zopounidis, "The corporate economic performance of environmentally eligible firms nexus climate change: An empirical research in a Bayesian VAR framework," Energies, vol. 15, no. 19, p. 7266, 2022.
• F. Aliu, J. Kučera, and S. Hašková, "Agricultural commodities in the context of the Russia-Ukraine war: Evidence from corn, wheat, barley, and sunflower oil," Forecasting, vol. 5, no. 1, pp. 351–373, 2023.
• S. S. Busnita, R. Oktaviani, and T. Novianti, "How far climate change affects the Indonesian paddy production and rice price volatility?" Int. J. Agric. Sci., vol. 1, no. 1, pp. 1–11, 2017.
• L. Zhang, H. Zhang, D. Liu, Q. Huang, J. Chang, and S. Liu, "The dynamic response of runoff to human activities and climate change based on a combined hierarchical structure hydrological model and vector autoregressive model," Agronomy, vol. 13, no. 2, p. 510, 2023.
• I. Ramli, S. Rusdiana, H. Basri, and A. A. Munawar, "Predicted rainfall and discharge using vector autoregressive models in water resources management in the High Hill Takengon," IOP Conf. Ser.: Earth Environ. Sci., vol. 273, no. 1, p. 012009, 2019.
• A. Nugroho, S. Hartati, and K. Mustofa, "Vector autoregression (VAR) model for rainfall forecast and isohyet mapping in Semarang–Central Java–Indonesia," Int. J. Adv. Comput. Sci. Appl., vol. 5, no. 11, pp. 44-49, 2014.
• M. A. Shahin, M. A. Ali, and A. S. Ali, “Vector Autoregression (VAR) modeling and forecasting of temperature, humidity, and cloud coverage,” in Computational Intelligence Techniques in Earth and Environmental Sciences, T. Islam, P. Srivastava, M. Gupta & X. Zhu (eds.), Springer, Dordrecht, pp. 29–51, 2014.
• M. J. Hossain, A. A. Al Amin, and A. H. M. S. Islam, "Modeling and forecasting of climatic parameters: Univariate SARIMA versus multivariate vector autoregression approach," J. Bangladesh Agric. Univ., vol. 16, no. 1, pp. 131–143, 2018.
• S. Çiftçi and G. D. Batur Şir, "Acil servise başvuru sayısının zaman serisi analiz ve makine öğrenmesi yöntemleri ile tahmin edilmesine yönelik bir uygulama," Pamukkale Univ. Müh. Bilim. Derg., vol. 29, no. 7, pp. 667–679, 2023, doi: 10.5505/pajes.2022.18488.
• S. M. Norrulashikin, F. Yusof, and I. L. Kane, "An investigation towards the suitability of vector autoregressive approach on modeling meteorological data," Mod. Appl. Sci., vol. 9, no. 11, pp. 89–100, 2015.
• R. F. Engle and C. W. J. Granger, "Co-integration and error correction: Representation, estimation, and testing," Econometrica, vol. 55, no. 2, pp. 251–276, 1987.
• C. A. Sims, "The role of approximate prior restrictions in distributed lag estimation," J. Amer. Stat. Assoc., vol. 67, no. 337, pp. 169–175, 1972.
• X. Chang, M. Gao, Y. Wang, and X. Hou, "Seasonal autoregressive integrated moving average model for precipitation time series," J. Math. Stat., vol. 8, no. 4, pp. 500–505, 2012.
• K. Kotzé, Time Series Modelling Notes, 2025. [Online]. Available: https://kevin-kotze.gitlab.io/tsm/ts-10-note/. [Accessed: Mar. 12, 2025].
• R-Econometrics, "Introduction to Vector Error Correction Models (VECM)," 2025. [Online]. Available: https://www.r-econometrics.com/time%20series/vecintro/. [Accessed: Mar. 12, 2025].
• G. Box and G. M. Jenkins, Analysis: Forecasting and Control, San Francisco: Holden-Day, 1976.
• A. Demirtop and O. Sevli, "Wind speed prediction using LSTM and ARIMA time series analysis models: A case study of Gelibolu," Turk. J. Eng., vol. 8, no. 3, pp. 524–536, 2024.
• F. J. Anscombe, "Graphs in statistical analysis," Amer. Stat., vol. 27, no. 1, pp. 17–21, 1973.
• D. Ollech and K. Webel, "A random forest-based approach to identifying the most informative seasonality tests," Deutsche Bundesbank’s Discussion Paper Series, no. 55/2020, 2020.
• H. Akaike, "A new look at the statistical model identification," IEEE Trans. Autom. Control, vol. 19, no. 6, pp. 716–723, 1974.
• G. Schwarz, "Estimating the dimension of a model," Ann. Statist., vol. 6, no. 2, pp. 461–464, 1978.
• E. J. Hannan and B. G. Quinn, "The determination of the order of an autoregression," J. Roy. Stat. Soc. Ser. B (Methodol.), vol. 41, no. 2, pp. 190–195, 1979.
• H. Akaike, "Statistical predictor identification," Ann. Inst. Stat. Math., vol. 22, no. 1, pp. 203–217, 1970, doi: 10.1007/BF02532251.
• C. W. J. Granger, "Investigating causal relations by econometric models and cross-spectral methods," Econometrica, vol. 37, no. 3, pp. 424–438, 1969.
• S. Johansen, "Statistical analysis of cointegration vectors," J. Econ. Dyn. Control, vol. 12, no. 2–3, pp. 231–254, 1988.