Implementing Histogram of Oriented Gradients to Recognize Crypto Price Graphic Patterns with Artificial Neural Network - Wibowo, Suluh Arif; Rachmat, Nur (Information Technology and Science (ITScience), 2024)
Technical analysis stands as a pivotal strategy in analyzing graphic patterns to forecast future movements in crypto asset prices. However, comprehending numerous patterns poses a significant challenge for novice investors venturing into the investment realm. This study aims to facilitate investors in recognizing crypto price graph forms by classifying cryptographic price chart patterns. The dataset comprises images of seven types of crypto price graphic patterns obtained from the Kagle website, totaling 210 data points. A 70:30 training and testing data split is employed to ensure robust model evaluation. The study explores nine different Histogram of Oriented Gradients (HOG) parameter combinations for graphic pattern extraction. Leveraging the artificial neural network (ANN) classification method with parameter hyper tuning, the study assesses various HOG parameter configurations to optimize classification performance. The most optimal results are achieved with parameters Bin = 9, Cell Size = 16x16, and Block Size = 1x1, boasting an accuracy rate of 95.23%, precision of 95.55%, and recall of 95.23%. This classification approach streamlines the process for investors, enabling them to discern crypto price graph patterns effectively, thereby enhancing their investment decision-making capabilities in the dynamic cryptocurrency market landscape. By providing a structured method for pattern recognition, this study contributes to democratizing access to technical analysis tools, particularly benefiting novice investors seeking to navigate the complexities of cryptocurrency investment.