Crypto-Currency Price Forecasting

Date of Award

5-5-2024

Degree Name

M.S. in Computer Science

Department

Department of Computer Science

Advisor/Chair

Tam Nguyen

Abstract

This thesis investigates novel approaches to raise the precision and dependability of forecasting models while delving into the intricate realm of bitcoin price forecasting. It proposes a novel method combining trend analysis and manual analysis, with a focus on Bitcoin, a trailblazing cryptocurrency. The process converts daily price fluctuations into binary numbers that represent the mood of the market. After applying manual analysis to these trends, the shared coefficient is obtained that indicates the weighted influence of market patterns on future pricing. Then, a forecasting model that projects Bitcoin values for the eleventh day uses the derivative coefficient. This technique offers a comprehensive view of the dynamics of the bitcoin market by bridging the gap between qualitative trend analysis and quantitative modeling. This thesis not only compares and improves upon the suggested method but also tests it against conventional windowing strategies. The findings provide insight into the fundamental patterns of cryptocurrency pricing and demonstrate the efficacy of the trend-based linear regression and manual analysis technique. It advances the developing area of bitcoin analysis by laying the groundwork for more precise forecasting models and offering insightful information about market behavior.

Keywords

Computer Science

Rights Statement

Copyright 2024, author

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