[论文解读] Weather Prediction Using CNN-LSTM for Time Series Analysis: A Case Study on Delhi Temperature Data

Bangyu Li, Qian Yang|arXiv (Cornell University)|Sep 14, 2024

Neural Networks and Applications被引用 7

一句话总结

本文提出一种混合 CNN-LSTM 模型，使用历史数据（1996–2017）预测德里温度，相比传统方法显示出更高的准确性和稳定性。

ABSTRACT

As global climate change intensifies, accurate weather forecasting is increasingly crucial for sectors such as agriculture, energy management, and environmental protection. Traditional methods, which rely on physical and statistical models, often struggle with complex, nonlinear, and time-varying data, underscoring the need for more advanced techniques. This study explores a hybrid CNN-LSTM model to enhance temperature forecasting accuracy for the Delhi region, using historical meteorological data from 1996 to 2017. We employed both direct and indirect methods, including comprehensive data preprocessing and exploratory analysis, to construct and train our model. The CNN component effectively extracts spatial features, while the LSTM captures temporal dependencies, leading to improved prediction accuracy. Experimental results indicate that the CNN-LSTM model significantly outperforms traditional forecasting methods in terms of both accuracy and stability, with a mean square error (MSE) of 3.26217 and a root mean square error (RMSE) of 1.80615. The hybrid model demonstrates its potential as a robust tool for temperature prediction, offering valuable insights for meteorological forecasting and related fields. Future research should focus on optimizing model architecture, exploring additional feature extraction techniques, and addressing challenges such as overfitting and computational complexity. This approach not only advances temperature forecasting but also provides a foundation for applying deep learning to other time series forecasting tasks.

研究动机与目标

• 在气候变化及其对农业、能源等行业的影响下，推动温度预测的准确性。
• 研究一种混合 CNN-LSTM 方法，以捕捉历史德里气象数据中的空间特征和时间依赖关系。
• 将 CNN-LSTM 模型与传统预测方法进行比较，以评估性能提升。

提出的方法

• 对1996年至2017年的历史气象数据进行预处理和探索。
• 使用 CNN 组件从数据中提取空间特征。
• 应用 LSTM 组件建模序列中的时间依赖性。
• 训练并评估混合模型，报告 MSE 和 RMSE。
• 讨论混合模型如何在准确性和稳定性方面优于传统方法。

实验结果

研究问题

• RQ1 与传统方法相比，CNN-LSTM 混合模型是否能提升德里温度预测的准确性？
• RQ2 空间特征提取和时间依赖建模如何提升预测性能？
• RQ3 所提模型在德里温度数据集上的准确性和稳定性提升（如 MSE、RMSE）是多少？

主要发现

• CNN-LSTM 模型在准确性和稳定性方面显著优于传统预测方法。
• 报告的评估指标：MSE = 3.26217，RMSE = 1.80615。
• 该方法展示了成为气象预测及其他时间序列任务的鲁棒工具的潜力。