Time Series Prediction of Temperature Using Seasonal ARIMA and LSTM Models

Year 2023, Volume: 9 Issue: 3, 574 - 584, 01.01.2024

Hakan Koçak

Abstract

Precise quantitative understanding and monitoring of temperature is indispensable due to its tremendous impact on almost every aspect of our lives. This work investigates prediction capabilities of two machine learning techniques, namely Autoregressive Integrated Moving Average (ARIMA) and Long Short Term Memory (LSTM) and compares them in predicting monthly mean temperature time series data for a weather station in Ankara, Türkiye from January 2010 to March 2023. The comparison of forecasting performance was based on mean square error (MSE), root mean square error (RMSE) and mean absolute error (MAE). The results showed that both models can capture the variations of time series data. Both models exhibited reasonably good performance in predicting monthly mean temperature, but the ARIMA model gave the least forecast error compared to the LSTM model.

Keywords

Time series prediction, monthly mean temperature, ARIMA, SARIMA, LSTM, Autocorrelation Function (ACF)

ARIMA ve LSTM Modelleri ile Sıcaklık Zaman Serileri Tahmini

Abstract

Hava sıcaklığının insan hayatının hemen her alanındaki büyük öneminden dolayı sıcaklığın nicel olarak anlaşılması ve takip edilmesi oldukça elzemdir. Bu çalışmada Ankara’da bulunan bir meteorolojik hava istasyonundan elde edilen Ocak 2010 ila Mart 2023 tarihleri arasındaki gözlem verileri kullanarak ARIMA (Autoregressive Integrated Moving Average) ve LSTM (Long Short Term Memory) makine öğrenmesi metotlarıyla aylık ortalama hava sıcaklığını kestirimi yapılmış ve bu metotların sıcaklık tahmini konusundaki başarısı karşılaştırılarak irdelenmiştir. Modellerin tahmin başarıları Ortalama Karesel Hata (OKH), Ortalama Karesel Hata (KOKH) ve Ortalama Mutlak Hata performans metrikleri kullanarak yapılmıştır. Araştırma sonucunda, aylık ortalama sıcaklık kestiriminde her iki modelin de iyi derecede performans gösterdiği görülmekle birlikte ARIMA modelinin LSTM modeline göre hata oranının daha az olduğu, dolayısıyla daha iyi performans gösterdiği ortaya çıkmıştır.

Keywords

— Zaman serileri tahmini, aylık ortalama yağış, ARIMA, SARIMA, LSTM, Autocorrelation Function (ACF)

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