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Meeting-ID: 280 519 2121
Passwort: 584190

Abstract:

This paper examines a novel AI-based computer vision approach for pattern recognition in financial time series classification. We transform past Bitcoin price sequences to Gramian Angular Field images and use them as input data for a convolutional neural network (CNN). We apply spectral relevance analysis to identify clusters of the decision behavior of the CNN. Clustering the images according to the associated relevance maps allows the comparison of cluster-based performances. We detect clusters with substantially higher predictive performance compared to the complete data set. The associated relevance matrices for each cluster represent favourable patterns for price prediction and are identified via the associated clusters.