按相似性分组加密货币数据，用注意力模型提升价格预测准确率。
Leveraging Time Series Categorization and Temporal Fusion Transformers to Improve Cryptocurrency Price Forecasting
- 将加密货币时间序列按行为相似性聚类，分组建模提高预测精度。
- 通过合并同类币种数据扩充训练集，缓解小样本导致的过拟合问题。
- 适合关注量化交易和资产配置的开发者与金融研究人员。
加密货币组合管理与交易决策至关重要，而准确预测价格是关键挑战。本文将金融时间序列按相似行为划分为若干子系列，对每类子系列构建基于注意力机制的深度学习模型进行下一步预测。由于加密货币数据量有限，类别增多会导致每类训练数据减少，复杂模型因参数量大难以充分训练。为此，我们提出通过整合其他同类加密货币的数据来扩充每类的训练样本，从而提升各分类模型的预测准确性。
原文摘要 · Abstract (English)
Organizing and managing cryptocurrency portfolios and decision-making on transactions is crucial in this market. Optimal selection of assets is one of the main challenges that requires accurate prediction of the price of cryptocurrencies. In this work, we categorize the financial time series into several similar subseries to increase prediction accuracy by learning each subseries category with similar behavior. For each category of the subseries, we create a deep learning model based on the attention mechanism to predict the next step of each subseries. Due to the limited amount of cryptocurrency data for training models, if the number of categories increases, the amount of training data for each model will decrease, and some complex models will not be trained well due to the large number of parameters. To overcome this challenge, we propose to combine the time series data of other cryptocurrencies to increase the amount of data for each category, hence increasing the accuracy of the models corresponding to each category.
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