用混合模型预测15种加密货币价格，融合上下文与外部变量。
Forecasting Cryptocurrency Prices using Contextual ES-adRNN with Exogenous Variables
- 双路径架构：主路径用带注意力的RNN捕捉时序依赖，上下文路径提供动态信息支持。
- 在15种加密货币上实现日、周、月度预测，生成点预测与置信区间。
- 适合关注多币种短期价格波动的量化交易者或金融建模研究者。
本文提出一种新型混合模型，用于多变量加密货币价格预测，结合指数平滑（ES）与循环神经网络（RNN）。模型包含两条路径：上下文路径从代表性序列中提取信息，主路径则接收这些信息及外部变量的动态调整信号。采用堆叠式带有层级膨胀的RNN结构，引入近期发展的注意力膨胀循环单元，以捕捉跨时间序列的短长期依赖关系，并动态加权输入信息。该模型可生成一天、一周和四周的点预测与预测区间。我们在15种加密货币上使用17个输入变量进行实验，与多种统计与机器学习模型对比，验证了其有效性。
原文摘要 · Abstract (English)
In this paper, we introduce a new approach to multivariate forecasting cryptocurrency prices using a hybrid contextual model combining exponential smoothing (ES) and recurrent neural network (RNN). The model consists of two tracks: the context track and the main track. The context track provides additional information to the main track, extracted from representative series. This information as well as information extracted from exogenous variables is dynamically adjusted to the individual series forecasted by the main track. The RNN stacked architecture with hierarchical dilations, incorporating recently developed attentive dilated recurrent cells, allows the model to capture short and long-term dependencies across time series and dynamically weight input information. The model generates both point daily forecasts and predictive intervals for one-day, one-week and four-week horizons. We apply our model to forecast prices of 15 cryptocurrencies based on 17 input variables and compare its performance with that of comparative models, including both statistical and ML ones.
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