K-Nearest Neighbors Algorithm for Analyzing Doge Coin Market Behavior
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This study investigates the application of the K-Nearest Neighbors (KNN) algorithm to analyze Dogecoin's market behavior using historical trading data, including daily metrics such as Open, High, Low, Close, and Volume, spanning from November 2017. As a proximity-based machine learning algorithm, KNN effectively captures short-term market patterns, achieving a low Mean Absolute Error (MAE) of 0.0017, demonstrating its capability in identifying general trends during stable periods. However, the model faces challenges in predicting sudden price shifts caused by external factors like social media sentiment and regulatory news, highlighting its limitations in highly volatile cryptocurrency markets. Preprocessing steps, including normalization and outlier handling, improved the algorithm’s performance, yet its scalability and sensitivity to hyperparameters remain issues. Future research directions include integrating external data sources, such as social media sentiment and macroeconomic indicators, and adopting advanced models like Gradient Boosting Machines (GBMs) or Long Short-Term Memory (LSTM) networks to enhance predictive accuracy and adaptability. These improvements aim to provide more robust insights into Dogecoin's market dynamics, aiding traders and financial analysts in navigating the complexities of cryptocurrency markets.
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