[论文解读] Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model

Jiajiang Shen, Weiyan Wu+1 more|arXiv (Cornell University)|Dec 11, 2024

Hydrological Forecasting Using AI被引用 5

一句话总结

一个用于时间序列温度预测的多尺度 CNN-LSTM-attention 架构，在 Eastern China 实现 MSE of 1.978295 和 RMSE of 0.8106562。

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.

研究动机与目标

• 在非线性和复杂气候数据中解决准确天气预测的需求。
• 开发一个将空间特征提取、时间序列建模和注意力机制整合在一起的深度学习模型，以突出重要特征。
• 将模型应用于中国的温度时间序列数据，以提高预测准确性。
• 通过更好的天气预测，展示深度学习在城市规划、农业和能源管理中的潜力。

提出的方法

• 构建一个多尺度 CNN-LSTM-Attention 神经网络架构。
• 将 CNN 用于空间特征提取，与 LSTM 共同捕捉时间依赖关系。
• 加入注意力机制以聚焦显著特征。
• 对数据进行预处理、执行特征提取和模型训练。
• 使用包括 MSE 和 RMSE 在内的标准回归指标进行评估。

实验结果

研究问题

• RQ1 一个多尺度 CNN-LSTM-Attention 模型是否能相较于基线方法改善 Eastern China 的温度时间序列预测？
• RQ2 多尺度 CNN 组件和注意力对气候指标的预测准确性有何影响？
• RQ3 MSE 和 RMSE 值如何反映模型在预测 Eastern China 温度趋势中的性能？

主要发现

• 该模型实现了 MSE of 1.978295。
• 该模型实现了 RMSE of 0.8106562。
• 该方法在预测 Eastern China 的温度趋势方面显示出高精度。
• 该工作展示了深度学习在气象数据用于城市规划、农业和能源管理中的决策支持的可应用性。