论文标题
可解释的深卷积烛台学习者
Explainable Deep Convolutional Candlestick Learner
论文作者
论文摘要
烛台是给定时期价格变动的图形表示。交易者可以通过查看烛台模式来发现资产的趋势。尽管深度卷积神经网络在认识烛台模式方面取得了巨大的成功，但它们的推理隐藏在黑匣子中。交易者无法确保该模型学到了什么。在这项贡献中，我们提供了一个框架，以解释确定时间序列的特定烛台模式的学习模型的推理。根据本地搜索对抗性攻击，我们表明，学识渊博的模型以类似于人类商人的方式感知了烛台的模式。
Candlesticks are graphical representations of price movements for a given period. The traders can discovery the trend of the asset by looking at the candlestick patterns. Although deep convolutional neural networks have achieved great success for recognizing the candlestick patterns, their reasoning hides inside a black box. The traders cannot make sure what the model has learned. In this contribution, we provide a framework which is to explain the reasoning of the learned model determining the specific candlestick patterns of time series. Based on the local search adversarial attacks, we show that the learned model perceives the pattern of the candlesticks in a way similar to the human trader.
