Abstract Many traders participate in activities known as "day-trading", trading Bitcoin against the dollar bill as the United States Dollar (USD) on very short timeframes to squeeze out profits from small market fluctuations. This paper aims to help traders decide how to best act by creating a model that can predict price movement's direction for the next 5-min time frame. Several machine-learning models have been tested for this Up/Down binary-classification problem. In this paper, we provide a comparison of the state-of-art strategies in predicting the movement direction for bitcoin, including Random Guessing and a Momentum-Based Strategy. The tested models include Autoregressive Integrated Moving Average (ARIMA), Prophet (by Facebook), Random Forest, Random Forest Lagged-Auto-Regression, and Multi-Layer Perceptron (MLP) Neural Networks. The MLP deep neural network has achieved the highest accuracy of 54% compared to other time-series prediction models. Also, in this paper, various data transformation and feature engineering have been applied in the comparison.

中文翻译：

---

预测比特币的市场走势：时间序列建模方法的比较

摘要 许多交易者参与被称为“日内交易”的活动，在很短的时间内将比特币兑美元作为美元 (USD) 进行交易，以从市场小幅波动中榨取利润。本文旨在通过创建一个模型来帮助交易者决定如何最好地采取行动，该模型可以预测下一个 5 分钟时间范围内的价格走势方向。已经针对这个向上/向下二元分类问题测试了几种机器学习模型。在本文中，我们比较了预测比特币运动方向的最新策略，包括随机猜测和基于动量的策略。测试的模型包括自回归综合移动平均 (ARIMA)、Prophet (by Facebook)、Random Forest、Random Forest Lagged-Auto-Regression、和多层感知器 (MLP) 神经网络。与其他时间序列预测模型相比，MLP 深度神经网络达到了 54% 的最高准确率。此外，在本文中，在比较中应用了各种数据转换和特征工程。