Forecasting Climate Trends with Stochastic Models: Comparative Analysis of TRAMO/SEATS Approach vs. Seasonal ARIMA Models
Environmental Modeling and Assessment, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s10666-026-10170-1
- Dergi Adı: Environmental Modeling and Assessment
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, ABI/INFORM, Aerospace Database, Compendex, Environment Index, Geobase, Greenfile, Natural Science Collection (ProQuest), Materials Science & Engineering Collection (ProQuest), Pharma Collection (ProQuest), Technology Collection (ProQuest)
- Anahtar Kelimeler: Forecasting, Meteorological time series, RegARIMA, SARIMA, TRAMO/SEATS
- Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
- Erciyes Üniversitesi Adresli: Evet
Özet
Time series analysis plays a crucial role in understanding climate variability and supporting reliable forecasting efforts. In this study, the forecasting performance of TRAMO/SEATS-based RegARIMA and SARIMA models is evaluated using long-term meteorological data (1960–2020) from Beyşehir and Seydişehir stations in Konya, Türkiye. The TRAMO (Time Series Regression with ARIMA Noise, Missing Observations, and Outliers) approach corrects for outliers, missing data, and deterministic components, while SEATS (Signal Extraction in ARIMA Time Series) extract stochastic components such as trend and seasonality. The seasonality structure of the data is assessed using the Webel-Ollech test, and stationarity is examined via the Augmented Dickey-Fuller test. Following seasonal adjustment with TRAMO/SEATS, more robust forecasting models are constructed. Comparative analyses indicate that TRAMO/SEATS-based RegARIMA models consistently outperform SARIMA models across most meteorological variables. For example, for maximum humidity, the TRAMO/SEATS approach reduced the mean squared error by approximately 65% (MSE = 0.397) compared to SARIMA (MSE = 1.15). Similarly, for maximum temperature, TRAMO/SEATS achieved lower forecasting errors (MSE = 1.784) than SARIMA (MSE = 2.631), demonstrating its superior ability to capture temperature dynamics. Overall, the TRAMO/SEATS-based models yielded lower Mean Squared Error (MSE: 0.25–5.22) and Root Mean Squared Error (RMSE: 0.50–2.29), along with higher Nash–Sutcliffe Efficiency values (NSE: 0.69–1.00). These findings demonstrate the superiority of the TRAMO/SEATS approach in modeling complex seasonal structures and improving forecast accuracy. This study provides a scientific basis for understanding regional climate dynamics and supports the development of effective climate change adaptation and risk mitigation strategies.