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

ArXiv ID: 2308.08558 “View on arXiv”

Authors: Unknown

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.

Keywords: Pattern Matching, Feature Engineering, Directional Prediction, Bitcoin, Multimodal Analysis

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: The paper uses relatively standard machine learning techniques (XGBoost) and technical analysis indicators without advanced mathematical derivations, but features a complete implementation with specific backtest-ready steps, dataset details, and performance metrics.

  flowchart TD
    A["Research Goal: Improve Bitcoin price direction prediction using multimodal pattern matching"] --> B["Data Collection & Preparation<br/>Historical Bitcoin OHLCV data"]
    B --> C["Key Methodology: PC Ranking<br/>Rank similar past chart patterns to current chart"]
    C --> D["Feature Engineering<br/>Generate directional features from ranked PCs"]
    D --> E["Computational Process<br/>Input multimodal features into predictive model"]
    E --> F["Key Outcome: Directional Prediction<br/>Enhanced model predicts future market direction"]
    F --> G["Conclusion<br/>Exploiting similar past patterns improves predictive capacity"]