Wasserstein distance loss function for financial time series deep learning

This paper presents user-friendly code for the implementation of a loss function for neural network time series models that exploits the topological structures of financial data. By leveraging the recently-discovered presence of topological features present in financial time series data, the code offers a more effective approach for creating forecasting models for such data given the fact that it allows neural network models to not only learn temporal patterns of the data, but also topological patterns. This paper aims to facilitate the adoption of the loss function proposed by Souto and Moradi (2024a) in financial time series by practitioners and researchers.