Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model

TL;DR

This paper introduces a multi-scale CNN-LSTM-Attention deep learning model that significantly improves temperature prediction accuracy in Eastern China by effectively capturing complex climate data patterns.

Contribution

The paper presents a novel multi-scale CNN-LSTM-Attention architecture tailored for temperature time series forecasting, enhancing prediction accuracy over traditional methods.

Findings

High prediction accuracy with MSE of 1.978295

Effective integration of CNN, LSTM, and attention mechanisms

Model outperforms existing weather forecasting models

Abstract

In recent years, the importance of accurate weather forecasting has become increasingly prominent due to the impacts of global climate change and the rapid development of data science. Traditional forecasting methods often struggle to handle the complexity and nonlinearity inherent in climate data. To address these challenges, we propose a weather prediction model based on a multi-scale convolutional CNN-LSTM-Attention architecture, specifically designed for time series forecasting of temperature data in China. The model integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and attention mechanisms to leverage the strengths of spatial feature extraction, temporal sequence modeling, and the ability to focus on important features. The development process of the model includes data collection, preprocessing, feature extraction, and model building. Experimental results show that the model performs excellently in predicting temperature trends with high accuracy. The final computed results indicate that the Mean Squared Error (MSE) is 1.978295 and the Root Mean Squared Error (RMSE) is 0.8106562. This work marks a significant advancement in applying deep learning techniques to meteorological data, offering a valuable tool for improving weather forecasting accuracy and providing essential support for decision-making in areas such as urban planning, agriculture, and energy management.