Weather Prediction Using CNN-LSTM for Time Series Analysis: A Case Study on Delhi Temperature Data

• URL: http://arxiv.org/abs/2409.09414v1
• Date: Sat, 14 Sep 2024 11:06:07 GMT
• Title: Weather Prediction Using CNN-LSTM for Time Series Analysis: A Case Study on Delhi Temperature Data
• Authors: Bangyu Li, Yang Qian,
• Abstract summary: This study explores a hybrid CNN-LSTM model to enhance temperature forecasting accuracy for the Delhi region. We employed both direct and indirect methods, including comprehensive data preprocessing and exploratory analysis, to construct and train our model. Experimental results indicate that the CNN-LSTM model significantly outperforms traditional forecasting methods in terms of both accuracy and stability.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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