TDDF: HFMD Outpatients Prediction Based on Time Series Decomposition and Heterogenous Data Fusion in Xiamen, China

Hand, foot and mouth disease (HFMD) is a common infectious disease in global public health. In this paper, the time series decomposition and heterogeneous data fusion (TDDF) method is proposed to enhance features in the performance of HFMD outpatients prediction. The TDDF first represents meteorolog...

Full description

Saved in:
Bibliographic Details
Published inAdvanced Data Mining and Applications Vol. 11888; pp. 658 - 667
Main Authors Wang, Zhijin, Huang, Yaohui, He, Bingyan, Luo, Ting, Wang, Yongming, Lin, Yingxian
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2019
Springer International Publishing
SeriesLecture Notes in Computer Science
Subjects
Baidu search index
HFMD prediction
Meteorological factor
Online AccessGet full text
ISBN9783030352301
3030352307
ISSN0302-9743
1611-3349
DOI10.1007/978-3-030-35231-8_48

Cover

More Information
Summary:Hand, foot and mouth disease (HFMD) is a common infectious disease in global public health. In this paper, the time series decomposition and heterogeneous data fusion (TDDF) method is proposed to enhance features in the performance of HFMD outpatients prediction. The TDDF first represents meteorological features and Baidu search index features with the consideration of lags, then those features are fused into decomposed historical HFMD cases to predict coming outpatient cases. Experimental results and analyses on the real collected records show the efficiency and effectiveness of TDDF on regression methods.
ISBN:9783030352301
3030352307
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-35231-8_48