BIRP: Bitcoin Information Retrieval Prediction Model Based on Multimodal Pattern Matching
Minsuk Kim, Byungchul Kim, Junyeong Yong, Jeongwoo Park, Gyeongmin, Kim

TL;DR
This paper introduces BIRP, a multimodal pattern matching model that ranks past Bitcoin chart patterns to enhance directional prediction, aiming to improve investment decision-making in volatile markets.
Contribution
The paper proposes a novel ranking-based approach for pattern matching that improves Bitcoin price movement prediction by leveraging multimodal features.
Findings
Ranking similar past patterns enhances prediction accuracy.
Multimodal features improve directional prediction performance.
Application to Bitcoin demonstrates effectiveness in volatile markets.
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…
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Taxonomy
TopicsStock Market Forecasting Methods · Time Series Analysis and Forecasting · Data Stream Mining Techniques
