An Integrated Dual Attention with Convolutional LSTM for Short-Term Temperature Forecasting

Abstract

In recent years, there are multiple temperature predictive models have demonstrated the effectiveness and outperformances of applying different deep neural architectures, such as convolutional neural network (CNN) and recurrent neural network (RNN) for the temperature forecasting task in forms of time-series analysis problem in comparing with previous traditional machine learning based techniques. However, up to this time, there are still several limitations of existing deep learning-based temperature predictive methods related to the capability of efficiently integrating extra information resources into the temperature data learning process. Moreover, the high-noised/chaotic fluctuations within daily temperature data also lead to downgrades in the accuracy performance of recent deep learning-based techniques. To overcome these challenges, in this paper, we proposed a novel integrated dual attention mechanism with the Convolutional Long Short-Term Memory Network (LSTM), called as: DAttConvLSTM. Our proposed DAttCovLSTM supports to effectively capture the chaotic and dynamic temporal information from daily temperature data, thus significantly improve the prediction results. Extensive experiments and comparative studies in real-world datasets demonstrated the effectiveness of our proposed model in comparing with recent state-of-the-art baselines.

Keywords:

• Attention
• convolutional LSTM
• deep learning
• temperature forecasting

Additional information

Funding

This research is funded by Thu Dau Mot University, Binh Duong, Vietnam.