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

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

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