Lin Lin
ABSTRACT
In this paper, by using the dataset of people's livelihood appeal published by government, we construct a combined model of Decomposing Module and Long Short-Term Memory (DM-LSTM) neural network, and conduct the short-term analysis of people's livelihood appeal events and nowcasting of regional Gross Domestic Product (GDP). The experimental results show that the sequence decomposition algorithm has an impact on the prediction accuracy. The Wavelet Package Decomposition (WPD) and Variational Mode Decomposition (VMD) decomposition algorithms have better performance in the task of predicting people's livelihood appeal events, while the Empirical Wavelet Transform Decomposition (EWD) algorithm is more suitable for the task of regional GDP nowcasting.
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Index Terms
- Big Data Analysis and Mining For People's Livelihood Appeal
Index terms have been assigned to the content through auto-classification.
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