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Meta-forecasting is a newly emerging field which combines meta-learning and time series forecasting.
The tourism forecasting competition
G. Athanasopoulos, R. J. Hyndman, H. Song, and D. C. Wu · 2011
Earlier work this paper cites.
Uber tlc foil response dataset: https://github.com/fivethirtyeight/uber-tlc-foil-response, 2015
fivethirtyeight · 2015
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UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
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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.
Forecasting: principles and practice
R. J. Hyndman and G. Athanasopoulos · 2018
Earlier work this paper cites.
Modeling long-and short-term temporal patterns with deep neural networks
G. Lai, W.-C. Chang, Y. Yang, and H. Liu · 2018
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N-beats: Neural basis expansion analysis for interpretable time series forecasting
B. N. Oreshkin, D. Carpov, N. Chapados, and Y. Bengio · 2019
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Gluonts: Probabilistic and neural time series modeling in python
A. Alexandrov, K. Benidis, M. Bohlke-Schneider, V. Flunkert, J. Gasthaus, T. Januschowski, D. C. Maddix, S. S. Rangapuram, D. Salinas, J. Schulz, et al · 2020
Cited alongside, same era.
Generative adversarial networks
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2020
Cited alongside, same era.
Datsing: Data augmented time series forecasting with adversarial domain adaptation
H. Hu, M. Tang, and C. Bai · 2020
Cited alongside, same era.
Few-shot learning for time-series forecasting
T. Iwata and A. Kumagai · 2020
Cited alongside, same era.
Deepar: Probabilistic forecasting with autoregressive recurrent networks
D. Salinas, V. Flunkert, J. Gasthaus, and T. Januschowski · 2020
Cited alongside, same era.
Meta-learning framework with applications to zero-shot time-series forecasting
B. N. Oreshkin, D. Carpov, N. Chapados, and Y. Bengio · 2021
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A transformer-based framework for multivariate time series representation learning
G. Zerveas, S. Jayaraman, D. Patel, A. Bhamidipaty, and C. Eickhoff · 2021
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N-hits: Neural hierarchical interpolation for time series forecasting
C. Challu, K. G. Olivares, B. N. Oreshkin, F. Garza, M. Mergenthaler, and A. Dubrawski · 2022
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Domain adaptation for time series forecasting via attention sharing
X. Jin, Y. Park, D. Maddix, H. Wang, and Y. Wang · 2022
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Time series forecasting competition for computational intelligence, 2008
Neural Forecasting Competition · 2022
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H. Wang, H. He, and D. Katabi · 2020
Cited alongside, same era.
Meta-forecasting by combining global deeprepresentations with local adaptation
R. Grazzi, V. Flunkert, D. Salinas, T. Januschowski, M. Seeger, and C. Archambeau · 2021
Cited alongside, same era.
Forecasting: theory and practice
F. Petropoulos, D. Apiletti, V. Assimakopoulos, M. Z. Babai, D. K. Barrow, S. B. Taieb, C. Bergmeir, R. J. Bessa, J. Bijak, J. E. Boylan, et al · 2022
Later among the works it cites.
Ts2vec: Towards universal representation of time series
Z. Yue, Y. Wang, J. Duan, T. Yang, C. Huang, Y. Tong, and B. Xu · 2022
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