Fetching the paper…
Reading the bibliography…
Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set.
Box, G.E.P., Jenkins, G.M.: Some recent advances in forecasting and control. Journal of the Royal Statistical Society. Series C (Applied Statistics) 17
1968
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Krollner, B., Vanstone, B.J., Finnie, G.R., et al.: Financial time series forecasting with machine learning techniques: a survey. In: ESANN (2010)
2010
Earlier work this paper cites.
2014
Earlier work this paper cites.
Feurer, M., Springenberg, J., Hutter, F.: Initializing bayesian hyperparameter optimization via meta-learning. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 29 (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Santoro, A., Bartunov, S., Botvinick, M., Wierstra, D., Lillicrap, T.: Meta-learning with memory-augmented neural networks. In: International conference on machine learning. pp. 1842–1850. PMLR (2016)
2016
Earlier work this paper cites.
Vinyals, O., Blundell, C., Lillicrap, T., Wierstra, D., et al.: Matching networks for one shot learning. Advances in neural information processing systems 29
2016
Earlier work this paper cites.
Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1251–1258 (2017)
2017
Earlier work this paper cites.
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International conference on machine learning. pp. 1126–1135. PMLR (2017)
2017
Earlier work this paper cites.
Munkhdalai, T., Yu, H.: Meta networks. In: International Conference on Machine Learning. pp. 2554–2563. PMLR (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Snell, J., Swersky, K., Zemel, R.: Prototypical networks for few-shot learning. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in neural information processing systems. pp. 5998–6008 (2017)
2017
Earlier work this paper cites.
Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: A strong baseline. In: 2017 International joint conference on neural networks (IJCNN). pp. 1578–1585. IEEE (2017)
2017
Earlier work this paper cites.
Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R.R., Smola, A.J.: Deep sets. Advances in neural information processing systems 30
2017
Earlier work this paper cites.
2018
Earlier work this paper cites.
Drumond, R.R., Marques, B.A., Vasconcelos, C.N., Clua, E.: Peek-an lstm recurrent network for motion classification from sparse data. In: VISIGRAPP (1: GRAPP). pp. 215–222 (2018)
2018
Earlier work this paper cites.
Gupta, A., Mendonca, R., Liu, Y., Abbeel, P., Levine, S.: Meta-reinforcement learning of structured exploration strategies. Advances in neural information processing systems 31
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Makridakis, S., Spiliotis, E., Assimakopoulos, V.: The m4 competition: Results, findings, conclusion and way forward. International Journal of Forecasting 34
2018
Cited alongside, same era.
Makridakis, S., Spiliotis, E., Assimakopoulos, V.: Statistical and machine learning forecasting methods: Concerns and ways forward. PloS one 13
2018
Cited alongside, same era.
Narwariya, J., Malhotra, P., Vig, L., Shroff, G., Vishnu, T.: Meta-learning for few-shot time series classification. In: Proceedings of the 7th ACM IKDD CoDS and 25th COMAD. pp. 28–36 (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: N-beats: Neural basis expansion analysis for interpretable time series forecasting. In: International Conference on Learning Representations (2020), https://openreview.net/forum?id=r1ecqn4YwB
2020
Later among the works it cites.
Salinas, D., Flunkert, V., Gasthaus, J., Januschowski, T.: Deepar: Probabilistic forecasting with autoregressive recurrent networks. International Journal of Forecasting 36
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Rangapuram, S.S., Seeger, M.W., Gasthaus, J., Stella, L., Wang, Y., Januschowski, T.: Deep state space models for time series forecasting. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 31. Curran Associates, Inc. (2018), https://proceedings.neurips.cc/paper/2018/file/5cf68969fb67aa6082363a6d4e6468e2-Paper.pdf
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Von Birgelen, A., Buratti, D., Mager, J., Niggemann, O.: Self-organizing maps for anomaly localization and predictive maintenance in cyber-physical production systems. Procedia cirp 72
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Dau, H.A., Bagnall, A., Kamgar, K., Yeh, C.C.M., Zhu, Y., Gharghabi, S., Ratanamahatana, C.A., Keogh, E.: The ucr time series archive. IEEE/CAA Journal of Automatica Sinica 6
2019
Cited alongside, same era.
Hou, R., Chang, H., Ma, B., Shan, S., Chen, X.: Cross attention network for few-shot classification. Advances in Neural Information Processing Systems 32
2019
Cited alongside, same era.
Smyl, S.: A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. International Journal of Forecasting 36
2020
Later among the works it cites.
Tang, W., Liu, L., Long, G.: Interpretable time-series classification on few-shot samples. In: 2020 International Joint Conference on Neural Networks (IJCNN). pp. 1–8. IEEE (2020)
2020
Later among the works it cites.
Wang, Y., Yao, Q., Kwok, J.T., Ni, L.M.: Generalizing from a few examples: A survey on few-shot learning. ACM computing surveys (csur) 53
2020
Later among the works it cites.
Arango, S.P., Heinrich, F., Madhusudhanan, K., Schmidt-Thieme, L.: Multimodal meta-learning for time series regression. In: International Workshop on Advanced Analytics and Learning on Temporal Data. pp. 123–138. Springer (2021)
2021
Later among the works it cites.
Dai, Z., Liu, H., Le, Q., Tan, M.: Coatnet: Marrying convolution and attention for all data sizes. Advances in Neural IHou, R., Chang, H., Ma, B., Shan, S., and Chen, X. (2019). Cross attention network for few-shot classification. Advances in Neural Information Processing Systems, 32. Information Processing Systems 34
2021
Later among the works it cites.
2021
Later among the works it cites.
Jawed, S., Jomaa, H., Schmidt-Thieme, L., Grabocka, J.: Multi-task learning curve forecasting across hyperparameter configurations and datasets. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases. pp. 485–501. Springer (2021)
2021
Later among the works it cites.
Lim, B., Zohren, S.: Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A 379
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
Oh, J., Yoo, H., Kim, C., Yun, S.Y.: {BOIL}: Towards representation change for few-shot learning. In: International Conference on Learning Representations (2021), https://openreview.net/forum?id=umIdUL8rMH
2021
Later among the works it cites.
Tolstikhin, I.O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Yung, J., Steiner, A., Keysers, D., Uszkoreit, J., et al.: Mlp-mixer: An all-mlp architecture for vision. Advances in Neural Information Processing Systems (2021)
2021
Later among the works it cites.