Van İli Sıcaklık Tahmininde SARIMA Modeli: Geleceğe Dönük Bir Öngörü

Abstract

İklim, dünyadaki yaşamın sürdürülebilirliği açısından kritik bir rol oynamaktadır ve sürekli değişen iklim koşulları, gezegen için büyük bir tehdit oluşturmaktadır. Bu durum, gelecekteki iklim verilerinin doğru bir şekilde tahmin edilmesini zorlaştırmaktadır. Birçok algoritma geliştirilmiş olsa da, henüz tam anlamıyla iklim verileriyle ilgili doğru tahminler yapılabilmiş değildir. Zaman serisi analizi, özellikle ARIMA algoritması, gelecekteki sıcaklık verilerini tahmin etmek için kullanılmaktadır. Potansiyel modeller, otokorelasyon fonksiyonları ve kısmi otokorelasyon fonksiyonları ile belirlenirken, en uygun model AIC gibi performans ölçütlerine dayalı olarak seçilmektedir. Seçilen modelin doğruluğu, Dickey Fuller testi ile doğrulanmakta ve SARIMA (p, d, q) × (P, D, Q) m modeli, en düşük AIC değerine sahip olan ile belirlenmektedir. Son olarak, SARIMA modeli kullanılarak gelecekteki sıcaklık tahminleri yapılmakta ve tahmin doğruluğu, kök ortalama kare hatası (RMSE) ve ortalama kare hatası (MSE) gibi ölçütlerle değerlendirilmektedir. Bu çalışmada, Van iline ait sıcaklık verilerinin tahmini için mevsimsel ARIMA modeli kullanılmıştır. Veri analizi sürecinde, ADF ve KPSS testleriyle serinin durağan olup olmadığı kontrol edilmiş ve gerekli dönüşümler yapılmıştır. Elde edilen en uygun model SARIMA(1, 1, 1) × (1, 0, 1, 12) olarak belirlenmiş ve modelin doğruluğu test edilmiştir. Modelin tahmin doğruluğu RMSE ve MSE değerleri ile doğrulanmış ve gelecekteki sıcaklık tahminlerinin doğruluğu oldukça yüksek bulunmuştur. SARIMA modeli, Van ilindeki sıcaklık değişimlerinin doğru tahmin edilmesinde etkili bir araçtır ve gelecekteki iklim tahminleri için güvenilir bir model olarak kullanılabilir.

Keywords

• ARIMA modeli
• SARIMA modeli
• sıcaklık tahmini
• sınıflandırma
• zaman serileri.

SARIMA Model in Temperature Forecasting for Van Province: A Forward-Looking Prediction

Abstract

Climate plays a critical role in the sustainability of life on Earth, and the constantly changing climate conditions pose a significant threat to the planet. This makes it difficult to accurately predict future climate data. Although many algorithms have been developed, accurate predictions regarding climate data have not yet been fully achieved. Time series analysis, particularly the ARIMA (Autoregressive Integrated Moving Average) algorithm, is used to predict future temperature data. Potential models are determined using autocorrelation functions (ACF) and partial autocorrelation functions (PACF), while the best model is selected based on performance metrics such as the Akaike Information Criterion (AIC). The accuracy of the selected model is validated through the Dickey-Fuller (ADF) test, and the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) (p, d, q) × (P, D, Q) m model is determined based on the lowest AIC value. Finally, future temperature predictions are made using the SARIMA model, and prediction accuracy is evaluated using metrics such as Root Mean Squared Error (RMSE) and Mean Squared Error (MSE). In this study, the seasonal ARIMA model was used to predict temperature data for Van Province. During the data analysis process, stationarity of the series was tested using the ADF and KPSS tests, and necessary transformations were applied. The best model obtained was identified as SARIMA(1, 1, 1) × (1, 0, 1, 12), and the model's accuracy was tested with high precision. The prediction accuracy of the model was validated using RMSE and MSE values, and the accuracy of the future temperature predictions was found to be very high. The SARIMA model is an effective tool for accurately predicting temperature changes in Van Province and can be used as a reliable model for future climate predictions.

Keywords

• ARIMA model
• classification
• SARIMA model
• temperature prediction
• time series

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