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Time-series data in real-world settings typically exhibit long-range dependencies and are observed at non-uniform intervals.
Approximation of dynamical systems by continuous time recurrent neural networks
K.-i. Funahashi and Y. Nakamura · 1993
Earlier work this paper cites.
Multiscaled randomness: A possible source of 1/f noise in biology
J. M. Hausdorff and C.-K. Peng · 1996
Earlier work this paper cites.
Modelling irregularly spaced financial data: theory and practice of dynamic duration models
N. Hautsch · 2004
Earlier work this paper cites.
Differential equations driven by rough paths
T. J. Lyons, M. Caruana, and T. Lévy · 2007
Earlier work this paper cites.
A simple approximate long-memory model of realized volatility
F. Corsi · 2009
Earlier work this paper cites.
Uniqueness for the signature of a path of bounded variation and the reduced path group
B. Hambly and T. Lyons · 2010
Earlier work this paper cites.
Learning phrase representations using rnn encoder-decoder for statistical machine translation
K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio · 2014
Earlier work this paper cites.
Algorithmic and high-frequency trading
Á. Cartea, S. Jaimungal, and J. Penalva · 2015
Earlier work this paper cites.
Unitary evolution recurrent neural networks
M. Arjovsky, A. Shah, and Y. Bengio · 2016
Earlier work this paper cites.
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K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
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M. Henaff, A. Szlam, and Y. LeCun · 2016
Earlier work this paper cites.
Diffusion decision model: Current issues and history
R. Ratcliff, P. L. Smith, S. D. Brown, and G. McKoon · 2016
Earlier work this paper cites.
Calculation of iterated-integral signatures and log signatures
J. Reizenstein · 2017
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
Derivatives pricing using signature payoffs
I. P. Arribas · 2018
Earlier work this paper cites.
The uea multivariate time series classification archive, 2018
A. Bagnall, H. A. Dau, J. Lines, M. Flynn, J. Large, A. Bostrom, P. Southam, and E. Keogh · 2018
Earlier work this paper cites.
Antisymmetricrnn: A dynamical system view on recurrent neural networks
B. Chang, M. Chen, E. Haber, and E. H. Chi · 2018
Earlier work this paper cites.
Neural ordinary differential equations
R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud · 2018
Earlier work this paper cites.
Correcting for missing and irregular data in home-range estimation
C. Fleming, D. Sheldon, W. Fagan, P. Leimgruber, T. Mueller, D. Nandintsetseg, M. Noonan, K. Olson, E. Setyawan, A. Sianipar, et al · 2018
Earlier work this paper cites.
A signature-based machine learning model for distinguishing bipolar disorder and borderline personality disorder
I. Perez Arribas, G. M. Goodwin, J. R. Geddes, T. Lyons, and K. E. Saunders · 2018
Earlier work this paper cites.
The iisignature library: efficient calculation of iterated-integral signatures and log signatures
J. Reizenstein and B. Graham · 2018
Earlier work this paper cites.
Can recurrent neural networks warp time?
C. Tallec and Y. Ollivier · 2018
Earlier work this paper cites.
Generating long sequences with sparse transformers
R. Child, S. Gray, A. Radford, and I. Sutskever · 2019
Earlier work this paper cites.
Augmented neural odes
E. Dupont, A. Doucet, and Y. W. Teh · 2019
Earlier work this paper cites.
Deep signature transforms
P. Kidger, P. Bonnier, I. Perez Arribas, C. Salvi, and T. Lyons · 2019
Earlier work this paper cites.
Cheap orthogonal constraints in neural networks: A simple parametrization of the orthogonal and unitary group
M. Lezcano-Casado and D. Martınez-Rubio · 2019
Earlier work this paper cites.
Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting
S. Li, X. Jin, Y. Xuan, X. Zhou, W. Chen, Y.-X. Wang, and X. Yan · 2019
Earlier work this paper cites.
Latent ordinary differential equations for irregularly-sampled time series
Y. Rubanova, R. T. Chen, and D. K. Duvenaud · 2019
Earlier work this paper cites.
Time-series generative adversarial networks
J. Yoon, D. Jarrett, and M. Van der Schaar · 2019
Earlier work this paper cites.
Longformer: The long-document transformer
I. Beltagy, M. E. Peters, and A. Cohan · 2020
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Rethinking attention with performers
K. M. Choromanski, V. Likhosherstov, D. Dohan, X. Song, A. Gane, T. Sarlos, P. Hawkins, J. Q. Davis, A. Mohiuddin, L. Kaiser, et al · 2020
Cited alongside, same era.
Lipschitz recurrent neural networks
N. B. Erichson, O. Azencot, A. Queiruga, L. Hodgkinson, and M. W. Mahoney · 2020
Cited alongside, same era.
Transformers are rnns: Fast autoregressive transformers with linear attention
A. Katharopoulos, A. Vyas, N. Pappas, and F. Fleuret · 2020
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P. Kidger and T. Lyons · 2020
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State-dependent hawkes processes and their application to limit order book modelling
M. Morariu-Patrichi and M. S. Pakkanen · 2022
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Deepvol: Volatility forecasting from high-frequency data with dilated causal convolutions
F. Moreno-Pino and S. Zohren · 2022
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Transformer neural processes: Uncertainty-aware meta learning via sequence modeling
T. Nguyen and A. Grover · 2022
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Graph-coupled oscillator networks
T. K. Rusch, B. Chamberlain, J. Rowbottom, S. Mishra, and M. Bronstein · 2022
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Modeling irregular time series with continuous recurrent units
M. Schirmer, M. Eltayeb, S. Lessmann, and M. Rudolph · 2022
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Neural controlled differential equations for irregular time series
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Learning long-term dependencies in irregularly-sampled time series
M. Lechner and R. Hasani · 2020
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Fourier neural operator for parametric partial differential equations
Z. Li, N. B. Kovachki, K. Azizzadenesheli, K. Bhattacharya, A. Stuart, A. Anandkumar, et al · 2020
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Handling irregularly sampled longitudinal data and prognostic modeling of diabetes using machine learning technique
S. Perveen, M. Shahbaz, T. Saba, K. Keshavjee, A. Rehman, and A. Guergachi · 2020
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Coupled oscillatory recurrent neural network (cornn): An accurate and (gradient) stable architecture for learning long time dependencies
T. K. Rusch and S. Mishra · 2020
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Implicit neural representations with periodic activation functions
V. Sitzmann, J. Martel, A. Bergman, D. Lindell, and G. Wetzstein · 2020
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Monash university, uea, ucr time series extrinsic regression archive
C. W. Tan, C. Bergmeir, F. Petitjean, and G. I. Webb · 2020
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Á. Cartea, G. Duran-Martin, and L. Sánchez-Betancourt · 2023
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Low-rank extended kalman filtering for online learning of neural networks from streaming data
P. Chang, G. Duràn-Martín, A. Y. Shestopaloff, M. Jones, and K. Murphy · 2023
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Contiformer: Continuous-time transformer for irregular time series modeling
Y. Chen, K. Ren, Y. Wang, Y. Fang, W. Sun, and D. Li · 2023
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On the effectiveness of randomized signatures as reservoir for learning rough dynamics
E. M. Compagnoni, A. Scampicchio, L. Biggio, A. Orvieto, T. Hofmann, and J. Teichmann · 2023
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Projections of model spaces for latent graph inference
H. S. de Ocáriz Borde, A. Arroyo, and I. Posner · 2023
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Diffuser: efficient transformers with multi-hop attention diffusion for long sequences
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Mamba: Linear-time sequence modeling with selective state spaces
A. Gu and T. Dao · 2023
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A neural rde approach for continuous-time non-markovian stochastic control problems
M. Höglund, E. Ferrucci, C. Hernández, A. M. Gonzalez, C. Salvi, L. Sánchez-Betancourt, and Y. Zhang · 2023
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Traveling waves encode the recent past and enhance sequence learning
T. A. Keller, L. Muller, T. Sejnowski, and M. Welling · 2023
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Deep autoregressive models with spectral attention
F. Moreno-Pino, P. M. Olmos, and A. Artés-Rodríguez · 2023
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Resurrecting recurrent neural networks for long sequences
A. Orvieto, S. L. Smith, A. Gu, A. Fernando, C. Gulcehre, R. Pascanu, and S. De · 2023
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Learning pde solution operator for continuous modeling of time-series
Y. Park, J. Choi, C. Yoon, M. Kang, et al · 2023
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A survey on oversmoothing in graph neural networks
T. K. Rusch, M. M. Bronstein, and S. Mishra · 2023
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Sigformer: Signature transformers for deep hedging
A. Tong, T. Nguyen-Tang, D. Lee, T. M. Tran, and J. Choi · 2023
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Are transformers effective for time series forecasting?
A. Zeng, M. Chen, L. Zhang, and Q. Xu · 2023
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Transformers need glasses! information over-squashing in language tasks
F. Barbero, A. Banino, S. Kapturowski, D. Kumaran, J. G. Araújo, A. Vitvitskyi, R. Pascanu, and P. Veličković · 2024
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