Big Data Analysis and Mining For People's Livelihood Appeal

Lin Lin; Ning Li; Gaoming Lei; Wei Qin; Lixin Liang; Lu Shen

doi:10.1145/3624288.3624293

Big Data Analysis and Mining For People's Livelihood Appeal

Lin Lin, Ning Li, Gaoming Lei, Wei Qin, Lixin Liang, Lu Shen

Faculty of Applied Sciences

Shenzhen Technology University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	ICBDC 2023 - 2023 8th International Conference on Big Data and Computing
Publisher	Association for Computing Machinery
Pages	32-40
Number of pages	9
ISBN (Electronic)	9781450399975
DOIs	https://doi.org/10.1145/3624288.3624293
Publication status	Published - 26 May 2023
Event	8th International Conference on Big Data and Computing, ICBDC 2023 - Shenzhen, China Duration: 26 May 2023 → 28 May 2023

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	8th International Conference on Big Data and Computing, ICBDC 2023
Country/Territory	China
City	Shenzhen
Period	26/05/23 → 28/05/23

Keywords

Big Data Analysis and Mining
Decomposing Module and Long Short-Term Memory (DM-LSTM)
People's Livelihood Appeal

Access to Document

10.1145/3624288.3624293

Cite this

@inproceedings{ec6289b38894472ab27cd017108b5898,

title = "Big Data Analysis and Mining For People's Livelihood Appeal",

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.",

keywords = "Big Data Analysis and Mining, Decomposing Module and Long Short-Term Memory (DM-LSTM), People's Livelihood Appeal",

author = "Lin Lin and Ning Li and Gaoming Lei and Wei Qin and Lixin Liang and Lu Shen",

note = "Publisher Copyright: {\textcopyright} 2023 ACM.; 8th International Conference on Big Data and Computing, ICBDC 2023 ; Conference date: 26-05-2023 Through 28-05-2023",

year = "2023",

month = may,

day = "26",

doi = "10.1145/3624288.3624293",

language = "English",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "32--40",

booktitle = "ICBDC 2023 - 2023 8th International Conference on Big Data and Computing",

}

Lin, L, Li, N, Lei, G, Qin, W, Liang, L & Shen, L 2023, Big Data Analysis and Mining For People's Livelihood Appeal. in ICBDC 2023 - 2023 8th International Conference on Big Data and Computing. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 32-40, 8th International Conference on Big Data and Computing, ICBDC 2023, Shenzhen, China, 26/05/23. https://doi.org/10.1145/3624288.3624293

Big Data Analysis and Mining For People's Livelihood Appeal. / Lin, Lin; Li, Ning; Lei, Gaoming et al.
ICBDC 2023 - 2023 8th International Conference on Big Data and Computing. Association for Computing Machinery, 2023. p. 32-40 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Big Data Analysis and Mining For People's Livelihood Appeal

AU - Lin, Lin

AU - Li, Ning

AU - Lei, Gaoming

AU - Qin, Wei

AU - Liang, Lixin

AU - Shen, Lu

PY - 2023/5/26

Y1 - 2023/5/26

N2 - 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.

AB - 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.

KW - Big Data Analysis and Mining

KW - Decomposing Module and Long Short-Term Memory (DM-LSTM)

KW - People's Livelihood Appeal

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U2 - 10.1145/3624288.3624293

DO - 10.1145/3624288.3624293

M3 - Conference contribution

AN - SCOPUS:85180410332

T3 - ACM International Conference Proceeding Series

SP - 32

EP - 40

BT - ICBDC 2023 - 2023 8th International Conference on Big Data and Computing

PB - Association for Computing Machinery

T2 - 8th International Conference on Big Data and Computing, ICBDC 2023

Y2 - 26 May 2023 through 28 May 2023

ER -

Big Data Analysis and Mining For People's Livelihood Appeal

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Keywords

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