2 research outputs found
Explainable Deep Convolutional Candlestick Learner
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.Comment: Accepted by The 32nd International Conference on Software Engineering
& Knowledge Engineering (SEKE 2020), KSIR Virtual Conference Cener,
Pittsburgh, USA, July 9--July 19, 202
Adversarial Robustness of Deep Convolutional Candlestick Learner
Deep learning (DL) has been applied extensively in a wide range of fields.
However, it has been shown that DL models are susceptible to a certain kinds of
perturbations called \emph{adversarial attacks}. To fully unlock the power of
DL in critical fields such as financial trading, it is necessary to address
such issues. In this paper, we present a method of constructing perturbed
examples and use these examples to boost the robustness of the model. Our
algorithm increases the stability of DL models for candlestick classification
with respect to perturbations in the input data.Comment: arXiv admin note: text overlap with arXiv:2005.0673