From Word to Financial Time Series Embedding

Abstract

Pre-trained word vectors contributed to state of the art results in many Natural Language Processing tasks. In this work, we show that the same algorithms can be applied to the embedding of financial time series. Pre-trained vectors for financial time series are useful for visualizing an investable universe, embedding a portfolio of financial instruments, and can be used as context vectors in larger NLP networks.