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Financial time series have historically been assumed to be a martingaleprocess under the Random Walk hypothesis. Instead of making investmentdecisions using the raw prices alone, various multimodal pattern matchingalgorithms have been developed to help detect subtly hidden repeatable patternswithin the financial market. Many of the chart-based pattern matching toolsonly retrieve similar past chart (PC) patterns given the current chart (CC)pattern, and leaves the entire interpretive and predictive analysis, thusultimately the final investment decision, to the investors. In this paper, wepropose an approach of ranking similar PC movements given the CC informationand show that exploiting this as additional features improves the directionalprediction capacity of our model. We apply our ranking and directionalprediction modeling methodologies on Bitcoin due to its highly volatile pricesthat make it challenging to predict its future movements.

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