A financial embedded vector model and its applications to time series forecasting

Abstract

Inspired by the embedding representation in Natural Language Processing (NLP), we develop a financial embedded vector representation model to abstract the temporal characteristics of financial time series. Original financial features are discretized firstly, and then each set of discretized features is considered as a "word" of NLP, while the whole financial time series corresponds to the "sentence" or "paragraph". Therefore the embedded vector models in NLP could be applied to the financial time series. To test the proposed model, we use Radial Basis Functions (RBF) neural networks as regression model to predict financial series by comparing the financial embedding vectors as input with the original features. Numerical results show that the prediction accuracy of the test data is improved for about 4-6 orders of magnitude, meaning that the financial embedded vector has a strong generalization ability.