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.

中文翻译：

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可解释的深度卷积烛台学习器

烛台是给定时期内价格走势的图形表示。交易者可以通过查看烛形图来发现资产的趋势。尽管深度卷积神经网络在识别烛台模式方面取得了巨大成功，但其推理却隐藏在黑匣子中。交易者无法确定模型学到了什么。在此贡献中，我们提供了一个框架，用于解释确定时间序列的特定烛形模式的学习模型的原因。基于本地搜索对抗攻击，我们表明学习的模型以类似于人类交易者的方式感知烛台的模式。