Integrating AI and Statistical Models for Climate Time Series Forecasting

Bahaa Kareem Mohammed, Dhurgham Kareem Gharkan and Hassan Hadi Khayoon

Data and Metadata, 2025, vol. 4, 893

Abstract: Climate change is a pressing global challenge, and predicting its future patterns is essential for mitigation strategies. This study integrates synthetic and real-world climate datasets to develop predictive models. Specifically, we apply Long Short-Term Memory (LSTM) networks alongside ARIMA and SARIMA models to forecast global temperature anomalies. Synthetic data were generated using a Gaussian-based data simulator calibrated on historical NOAA/IPCC data, contributing 30% of the training set. Validation included Kolmogorov-Smirnov tests to ensure distributional similarity to real data. Preprocessing involved interpolation for missing values and stationarity checks using the Augmented Dickey-Fuller (ADF) test (p

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dbk:datame:v:4:y:2025:i::p:893:id:1056294dm2025893

DOI: 10.56294/dm2025893

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