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http://dx.doi.org/10.30693/SMJ.2021.10.2.16

MPIL: Market prediction through image learning of unstructured and structured data  

Lee, Yoon Seon (인하대학교 전기컴퓨터공학과)
Lee, Ju Hong (인하대학교 전기컴퓨터공학과)
Choi, Bum Ghi (인하대학교 전기컴퓨터공학과)
Song, Jae Won (밸류파인더스)
Publication Information
Smart Media Journal / v.10, no.2, 2021 , pp. 16-21 More about this Journal
Abstract
Financial time series analysis plays a very important role economically and socially in modern society and is an important task affecting global development, but due to difficulties such as a lot of noise and uncertainty, financial time series analysis prediction is a difficult research topic. In this paper, we propose a market prediction method (MPIL) by converting unstructured data and structured data into images. For market prediction, it analyzes SNS and news data, which is unstructured data for n days, and converts the market data, which is structured data, to an image with the GADF algorithm, and predicts an ultra-short market that predicts the price of n+1 days through image learning. MPIL has an average accuracy of 56%, which is higher than the 50% average accuracy of the model that predicts the market with LSTM by using sentiment analysis used for existing market forecasting.
Keywords
Sentiment analysis; Time series analysis; Market prediction; Deep learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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