Forecasting emerging technologies using data augmentation and deep learning

Abstract

Abstract Deep learning can be used to forecast emerging technologies based on patent data. However, it requires a large amount of labeled patent data as a training set, which is difficult to obtain due to various constraints. This study proposes a novel approach that integrates data augmentation and deep learning methods, which overcome the problem of lacking training samples when applying deep learning to forecast emerging technologies. First, a sample data set was constructed using Gartner’s hype cycle and multiple patent features. Second, a generative adversarial network was used to generate many synthetic samples (data augmentation) to expand the scale of the sample data set. Finally, a deep neural network classifier was trained with the augmented data set to forecast emerging technologies, and it could predict up to 77% of the emerging technologies in a given year with high precision. This approach was used to forecast emerging technologies in Gartner’s hype cycles for 2017 based on patent data from 2000 to 2016. Four out of six of the emerging technologies were forecasted correctly, showing the accuracy and precision of the proposed approach. This approach enables deep learning to forecast emerging technologies with limited training samples.

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