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BIRP: Bitcoin Information Retrieval Prediction Model Based on Multimodal Pattern Matching

14 August 2023
Minsuk Kim
Byungchul Kim
Junyeong Yong
Jeongwoo Park
Gyeongmin Kim
ArXiv (abs)PDFHTML
Abstract

Financial time series have historically been assumed to be a martingale process under the Random Walk hypothesis. Instead of making investment decisions using the raw prices alone, various multimodal pattern matching algorithms have been developed to help detect subtly hidden repeatable patterns within the financial market. Many of the chart-based pattern matching tools only retrieve similar past chart (PC) patterns given the current chart (CC) pattern, and leaves the entire interpretive and predictive analysis, thus ultimately the final investment decision, to the investors. In this paper, we propose an approach of ranking similar PC movements given the CC information and show that exploiting this as additional features improves the directional prediction capacity of our model. We apply our ranking and directional prediction modeling methodologies on Bitcoin due to its highly volatile prices that make it challenging to predict its future movements.

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