Transformer for Times Series: an Application to the S&P500

Pierre Brugiere and Gabriel Turinici

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Abstract: The transformer models have been extensively used with good results in a wide area of machine learning applications including Large Language Models and image generation. Here, we inquire on the applicability of this approach to financial time series. We first describe the dataset construction for two prototypical situations: a mean reverting synthetic Ornstein-Uhlenbeck process on one hand and real S&P500 data on the other hand. Then, we present in detail the proposed Transformer architecture and finally we discuss some encouraging results. For the synthetic data we predict rather accurately the next move, and for the S&P500 we get some interesting results related to quadratic variation and volatility prediction.

Date: 2024-03
New Economics Papers: this item is included in nep-big and nep-cmp
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Published in In: Arai, K. (eds) Advances in Information and Communication. FICC 2025. Lecture Notes in Networks and Systems, vol 1285. Springer, Cham

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