Generative modeling for time series via Schrödinger bridge
Mohamed Hamdouche (hamdouche@lpsm.paris),
Pierre Henry-Labordere (phl@hotmail.com) and
Huyên Pham (pham@lpsm.paris)
Additional contact information
Mohamed Hamdouche: LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité
Pierre Henry-Labordere: Qube RT
Huyên Pham: LPSM (UMR_8001) - Laboratoire de Probabilités, Statistique et Modélisation - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique - UPCité - Université Paris Cité
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Abstract:
We propose a novel generative model for time series based on Schrödinger bridge (SB) approach. This consists in the entropic interpolation via optimal transport between a reference probability measure on path space and a target measure consistent with the joint data distribution of the time series. The solution is characterized by a stochastic differential equation on finite horizon with a path-dependent drift function, hence respecting the temporal dynamics of the time series distribution. We can estimate the drift function from data samples either by kernel regression methods or with LSTM neural networks, and the simulation of the SB diffusion yields new synthetic data samples of the time series. The performance of our generative model is evaluated through a series of numerical experiments. First, we test with a toy autoregressive model, a GARCH Model, and the example of fractional Brownian motion, and measure the accuracy of our algorithm with marginal and temporal dependencies metrics. Next, we use our SB generated synthetic samples for the application to deep hedging on real-data sets. Finally, we illustrate the SB approach for generating sequence of images.
Keywords: generative models; time series; Schrödinger bridge; kernel estimation; deep hedging (search for similar items in EconPapers)
Date: 2023-04-07
New Economics Papers: this item is included in nep-big, nep-des, nep-ecm and nep-ets
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Published in Université Paris Cité. 2023
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