A Novel Multi-Factor Three-Step Feature Selection and Deep Learning Framework for Regional GDP Prediction: Evidence from China
Sustainability
; 14(8):4408, 2022.
Article
in English
| ProQuest Central | ID: covidwho-1810131
ABSTRACT
Gross domestic product (GDP) is an important index reflecting the economic development of a region. Accurate GDP prediction of developing regions can provide technical support for sustainable urban development and economic policy formulation. In this paper, a novel multi-factor three-step feature selection and deep learning framework are proposed for regional GDP prediction. The core modeling process is mainly composed of the following three steps:
In Step I, the feature crossing algorithm is used to deeply excavate hidden feature information of original datasets and fully extract key information. In Step II, BorutaRF and Q-learning algorithms analyze the deep correlation between extracted features and targets from two different perspectives and determine the features with the highest quality. In Step III, selected features are used as the input of TCN (Temporal convolutional network) to build a GDP prediction model and obtain final prediction results. Based on the experimental analysis of three datasets, the following conclusions can be drawn (1) The proposed three-stage feature selection method effectively improves the prediction accuracy of TCN by more than 10%. (2) The proposed GDP prediction framework proposed in the paper has achieved better forecasting performance than 14 benchmark models. In addition, the MAPE values of the models are lower than 5% in all cases.
Environmental Studies; GDP prediction; feature selection; deep learning; temporal convolutional network; Feature extraction; Accuracy; Macroeconomics; Trends; Microprocessors; Optimization; Machine learning; Time series; Economic growth; Prediction models; Economic indicators; Urban development; COVID-19; Economic policy; Market economies; Technical services; Economic development; Neural networks; Medical research; Support vector machines; Algorithms; Gross Domestic Product--GDP; Methods; Information processing; Sustainable development; Coronaviruses; Data sets; Economic forecasting; China
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Prognostic study
Language:
English
Journal:
Sustainability
Year:
2022
Document Type:
Article
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