Comparative Analysis of Temperature Prediction Models: Simple Models vs. Deep Learning Models
Year: 2025
Type: conference-paper
Abstract: Accurate and concise temperature prediction models have important applications in meteorological science, agriculture, energy, and electricity. This study aims to compare the performance of simple models and deep learning models in temperature prediction and explore whether simple models can replace deep learning models in specific scenarios to save computing resources. Based on 37 years of daily temperature time series data from 10 cities from 1987 to 2024, the Simple Moving Average (SMA), Seasonal Average Method with Lookback Years (SAM-Lookback), and Long Short-Term Memory (LSTM) models are fitted to evaluate the accuracy of simple models and deep learning models in temperature prediction. The performance of different models is intuitively compared by calculating the RMSE and Percentage Error of each city. The results show that LSTM has higher accuracy in most cities, but the prediction results of SMA and LSTM are similar and perform equally well, while SAM-Lookback is relatively weak.
Authors: Zibo Wang, Weiqi Zhang, Eugene Pinsky
Institution: Boston University
Language: English
Subfield: Environmental Engineering
Field: Environmental Science
Domain: Physical Sciences
SDG: Climate action
Open Access status: gold
OpenAlex ID: W4413358736