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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö > Á¤º¸Ã³¸®ÇÐȸ ³í¹®Áö ¼ÒÇÁÆ®¿þ¾î ¹× µ¥ÀÌÅÍ °øÇÐ

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Current Result Document : 7 / 43 ÀÌÀü°Ç ÀÌÀü°Ç   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) ¾ÏȣȭÆó Á¾°¡ ¿¹Ãø ¼º´É°ú ÀÔ·Â º¯¼ö °£ÀÇ ¿¬°ü¼º ºÐ¼®
¿µ¹®Á¦¸ñ(English Title) Understanding the Association Between Cryptocurrency Price Predictive Performance and Input Features
ÀúÀÚ(Author) ¹ÚÀçÇö   ¼­¿µ¼®   Jaehyun Park   Yeong-Seok Seo  
¿ø¹®¼ö·Ïó(Citation) VOL 11 NO. 01 PP. 0019 ~ 0028 (2022. 01)
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(Korean Abstract)
ÃÖ±Ù ¾ÏȣȭÆó°¡ ¸¹Àº ÁÖ¸ñÀ» ¹ÞÀ½¿¡ µû¶ó ¾ÏȣȭÆóÀÇ Á¾°¡ ¿¹Ãø ¿¬±¸µéÀÌ È°¹ßÈ÷ ÁøÇàµÇ°í ÀÖ´Ù. ƯÈ÷ µö ·¯´× ¸ðµ¨À» Àû¿ë½ÃÄÑ ¿¹Ãø ¼º´ÉÀ» ³ôÀÌ·Á´Â ¿¬±¸µéÀÌ Áö¼ÓµÇ°í ÀÖ´Ù. µö ·¯´× ¸ðµ¨ Áß ½Ã°è¿­ µ¥ÀÌÅÍ¿¡¼­ ³ôÀº ¿¹Ãø ¼º´ÉÀ» º¸ÀÌ´Â LSTM (Long Short-Term Memory) ¸ðµ¨ÀÌ ´Ù°¢µµ·Î ÀÀ¿ëµÇ°í ÀÖÀ¸³ª º¯µ¿¼ºÀÌ Å« ¾ÏȣȭÆó Á¾°¡ µ¥ÀÌÅÍ¿¡¼­´Â ³·Àº ¿¹Ãø ¼º´ÉÀ» º¸ÀδÙ. À̸¦ ÇØ°áÇϱâ À§ÇØ »õ·Î¿î ÀÔ·Â º¯¼ö¸¦ ã¾Æ³»°í, À̸¦ »ç¿ëÇÏ´Â Á¾°¡ ¿¹Ãø ¿¬±¸°¡ ¼öÇàµÇ°í ÀÖ´Ù. ±×·¯³ª µö ·¯´× ±â¹ÝÀÇ ¾ÏȣȭÆó Á¾°¡ ¿¹Ãø¿¡ »ç¿ëµÇ´Â µ¥ÀÌÅ͵éÀÇ °¢ ÀÔ·Â º¯¼öµéÀÌ ¿¹Ãø ¼º´É¿¡ ¹ÌÄ¡´Â ¿µÇâ·ÂÀ̳ª ÇнÀ¿¡ È¿À²ÀûÀÎ ÀÔ·Â º¯¼öµéÀÇ Á¶ÇÕ¿¡ °üÇÑ ¿¬±¸ »ç·Ê°¡ ºÎÁ·ÇÑ ½ÇÁ¤ÀÌ´Ù. µû¶ó¼­ º» ³í¹®¿¡¼­´Â Bitcoin°ú EthereumÀ» Æ÷ÇÔÇÑ 6°¡Áö ¾ÏȣȭÆóÀÇ ÃÖ±Ù µ¿Çâ ÀڷḦ ¼öÁýÇÏ¿´°í, Åë°è¿Í µö ·¯´×À» ÅëÇØ ÀÔ·Â º¯¼öµéÀÌ ¾ÏȣȭÆó Á¾°¡ ¿¹Ãø¿¡ ¹ÌÄ¡´Â ¿µÇâ·ÂÀ» ºÐ¼®ÇÑ´Ù. ½ÇÇè °á°ú ¸ðµç ¾ÏȣȭÆóÀÇ Á¾°¡ ¿¹Ãø ¼º´É Æò°¡¿¡¼­ Á¾°¡ º¯µ¿·üÀ» Á¦¿ÜÇÑ °³Àå°¡, °í°¡, Àú°¡, °Å·¡·®, Á¾°¡¸¦ Á¶ÇÕÇßÀ» ¶§ °¡Àå ¿ì¼öÇÑ ¼º´ÉÀ» º¸¿´´Ù.
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(English Abstract)
Recently, cryptocurrency has attracted much attention, and price prediction studies of cryptocurrency have been actively conducted. Especially, efforts to improve the prediction performance by applying the deep learning model are continuing. LSTM (Long Short-Term Memory) model, which shows high performance in time series data among deep learning models, is applied in various views. However, it shows low performance in cryptocurrency price data with high volatility. Although, to solve this problem, new input features were found and study was conducted using them, there is a lack of study on input features that drop predictive performance. Thus, in this paper, we collect the recent trends of six cryptocurrencies including Bitcoin and Ethereum and analyze effects of input features on the cryptocurrency price predictive performance through statistics and deep learning. The results of the experiment showed that cryptocurrency price predictive performance the best when open price, high price, low price, volume and price were combined except for rate of closing price fluctuation.
Å°¿öµå(Keyword) LSTM   Deep Learning   ÀÔ·Â º¯¼ö   ¾ÏȣȭÆó   °¡°Ý ¿¹Ãø   µ¥ÀÌÅÍ ºÐ¼®   LSTM   Deep Learning   Input Feature   Cryptocurrency   Price Prediction   Data Analysis  
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