Time Series Data Generation by Linear Response Model

Author:

Naritomi Yusuke12,Adachi Takanori2

Affiliation:

1. Mitsubishi UFJ Trust Investment Technology Institute Co., Ltd.

2. Tokyo Metropolitan University

Publisher

Japanese Society for Artificial Intelligence

Reference14 articles.

1. [Alcaraz 22] Alcaraz, J. M. L. and Strodthoff, N.: Diffusion-Based Time Series Imputation and Forecasting with Structured State Space Models, arXiv preprint arXiv:2208.09399 (2022)

2. [Arjovsky 17] Arjovsky, M., Chintala, S., , and Bottou, L.: Wasserstein Gan, arXiv preprint arXiv:1701.07875 (2017)

3. [Bilos 22] Bilos, M., Rasul, K., Schneider, A., Nevmyvaka, Y., and Gunnemann, S.: Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusion, arXiv preprint arXiv:2211.02590 (2022)

4. [Goodfellow 14] Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y.: Generative adversarial nets, Advances in Neural Information Processing Systems, Vol. 27, pp. 2672–2680 (2014)

5. [Ikeguchi 05] Ikeguchi, M., Ueno, J., Sato, M., and Kidera, A.: Protein Structural Change Upon Ligand Binding: Linear Response Theory, Phys. Rev. Lett., Vol. 94, (2005)

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