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A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting.
- Source :
- Entropy; Mar2022, Vol. 24 Issue 3, p335-335, 17p
- Publication Year :
- 2022
-
Abstract
- Compared with mechanism-based modeling methods, data-driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model in practical areas. Due to the noise and conflict, redundancy, and inconsistency of big time-series data, the forecasting accuracy may reduce on the contrary. This paper proposes a deep network by selecting and understanding data to improve performance. Firstly, a data self-screening layer (DSSL) with a maximal information distance coefficient (MIDC) is designed to filter input data with high correlation and low redundancy; then, a variational Bayesian gated recurrent unit (VBGRU) is used to improve the anti-noise ability and robustness of the model. Beijing's air quality and meteorological data are conducted in a verification experiment of 24 h PM2.5 concentration forecasting, proving that the proposed model is superior to other models in accuracy. [ABSTRACT FROM AUTHOR]
- Subjects :
- BAYESIAN analysis
COMPUTER input design
FORECASTING
BIG data
AIR quality
Subjects
Details
- Language :
- English
- ISSN :
- 10994300
- Volume :
- 24
- Issue :
- 3
- Database :
- Complementary Index
- Journal :
- Entropy
- Publication Type :
- Academic Journal
- Accession number :
- 156002234
- Full Text :
- https://doi.org/10.3390/e24030335