Abstract
Nowadays, financial markets produce a large amount of data, in the form of historical time series, which quantitative researchers have recently attempted at predicting with deep learning models. These models are constantly updated with new incoming data in an online fashion. However, artificial neural networks tend to exhibit poor adaptability, fitting the last seen trends, without keeping the information from the previous ones. Continual learning studies this problem, called catastrophic forgetting, to preserve the knowledge acquired in the past and exploiting it for learning new trends. This paper evaluates and highlights continual learning techniques applied to financial historical time series in a context of binary classification (upward or downward trend). The main state-of-the-art algorithms have been evaluated with data derived from a practical scenario, highlighting how the application of continual learning techniques allows for better performance in the financial field against conventional online approaches (Code is available at https://github.com/albertozurli/cl_timeseries.).
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Dates are reported as DD/MM/YY.
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Acknowledgement
Work supported by FF4EuroHPC: HPC Innovation for European SMEs, Project Call 1. FF4EuroHPC has received funding from the European High-Performance Computing Joint Undertaking (JU) under grant agreement No 951745.
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Zurli, A., Bertugli, A., Credi, J. (2023). Does Catastrophic Forgetting Negatively Affect Financial Predictions?. In: Nicosia, G., et al. Machine Learning, Optimization, and Data Science. LOD 2022. Lecture Notes in Computer Science, vol 13810. Springer, Cham. https://doi.org/10.1007/978-3-031-25599-1_37
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