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Multi-mode Retrieval Method for Big Data of Economic Time Series Based on Machine Learning Theory

  • Hai-ying Chen
  • Lan-fang GongEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 279)

Abstract

For traditional search methods affected by the index build time, resulting in poor search results, a multi-mode retrieval method for big data of economic time series based on machine learning theory is proposed. According to the good extensibility of big data, construct a retrieval model and use binary data conversion methods to match big data. The binary sequence is defined by the relationship between different data, the similarity of data features is calculated, and the candidate candidate sequence is filtered. Data with no similar features are filtered, and each sub-sequence set matching the pattern is given by similarity size. After the threshold is added, on the basis of slightly reducing the filtering amplitude, the calculation of the similarity matching in the big data retrieval process is greatly reduced, and combined with the fixed interval sampling matching method to determine the characteristics of big data, thereby realizing the machine learning theory. The multi-mode retrieval method for big data of economic time series based on machine learning theory retrieval. According to the experimental comparison results, the retrieval efficiency of the method can reach 95%, which provides effective help for large-scale retrieval of massive data.

Keywords

First machine learning Second economic time series Third big data Forth retrieval 

Notes

Fund Project

The 2018 annual scientific research project of Hubei Provincial Department of education. Based on modern statistical theory and machine learning theory, economic time series analysis is carried out (B2018371).

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Wuhan Institute of Design and SciencesWuhanChina
  2. 2.Guangdong Polytechnic of Water Resources and Electric EngineeringGuangzhouChina

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