Comparative Analysis of ARIMA and LSTM Models for Temperature Forecasting in Semi-Arid Regions: A Case Study of Machakos County, Kenya

Abstract

Accurate temperature forecasting is vital for agriculture, disaster management, and climate resilience, particularly in semi-arid regions like Machakos County, Kenya. Traditional forecasting models, such as the Auto Regressive Integrated Moving Average (ARIMA), have been widely used for both short- and long-term temperature predictions. However, with advancements in machine learning, there is a growing need to evaluate how modern methods compare to traditional approaches. This research focused on comparing the predictive accuracy of ARIMA and the Long Short-Term Memory (LSTM) model, a deep learning algorithm that uses a gating mechanism to capture non-linear patterns in data. The study used Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and the Diebold-Mariano statistical test to evaluate and compare the performance of models. Temperature data was obtained from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resource (NASA POWER) database, using GPS coordinates to retrieve location-specific data for Machakos County. To ensure robust analysis, Python libraries such as Statsmodels, Pandas, TensorFlow, NumPy, Keras, and Matplotlib were used for data processing, fitting of models, and visualization of results. Findings showed that LSTM performed better in long-term predictions, achieving higher accuracy for 30-day forecasts, while both models performed significantly equivalently in the short term of 7 days. These findings highlight the complementary strengths of traditional and deep learning models in addressing different forecasting needs.