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Deep learning has been actively applied to time series forecasting, leading to a deluge of new methods, belonging to the class of historical-value models.
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Rectified linear units improve restricted boltzmann machines
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Neural decomposition of time-series data for effective generalization
Godfrey, L. B. and Gashler, M. S · 2017
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Causal discovery from temporally aggregated time series
Gong, M., Zhang, K., Schölkopf, B., Glymour, C., and Tao, D · 2017
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Time-series extreme event forecasting with neural networks at uber
Laptev, N., Yosinski, J., Li, L. E., and Smyl, S · 2017
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Principles of business forecasting
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Optimization as a model for few-shot learning
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Amit, R. and Meir, R · 2018
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Meta-learning requires meta-augmentation
Rajendran, J., Irpan, A., and Jang, E · 2020
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Deepar: Probabilistic forecasting with autoregressive recurrent networks
Salinas, D., Flunkert, V., Gasthaus, J., and Januschowski, T · 2020
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Fourier features let networks learn high frequency functions in low dimensional domains
Tancik, M., Srinivasan, P., Mildenhall, B., Fridovich-Keil, S., Raghavan, N., Singhal, U., Ramamoorthi, R., Barron, J., and Ng, R · 2020
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Meta-learning without memorization
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Time series forecasting with gaussian processes needs priors
Corani, G., Benavoli, A., and Zaffalon, M · 2021
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Forecasting: principles and practice
Hyndman, R. J. and Athanasopoulos, G · 2018
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Modeling long-and short-term temporal patterns with deep neural networks
Lai, G., Chang, W.-C., Yang, Y., and Liu, H · 2018
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Forecasting at scale
Taylor, S. J. and Letham, B · 2018
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How to train your MAML
Antoniou, A., Edwards, H., and Storkey, A · 2019
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Meta-learning with differentiable closed-form solvers
Bertinetto, L., Henriques, J. F., Torr, P., and Vedaldi, A · 2019
Cited alongside, same era.
Enhancing the locality and breaking the memory bottleneck of transformer on time series forecasting
Li, S., Jin, X., Xuan, Y., Zhou, X., Chen, W., Wang, Y.-X., and Yan, X · 2019
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Dupont, E., Teh, Y. W., and Doucet, A · 2021
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Meta-forecasting by combining global deeprepresentations with local adaptation
Grazzi, R., Flunkert, V., Salinas, D., Januschowski, T., Seeger, M., and Archambeau, C · 2021
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Learned initializations for optimizing coordinate-based neural representations
Tancik, M., Mildenhall, B., Wang, T., Schmidt, D., Srinivasan, P. P., Barron, J. T., and Ng, R · 2021
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Tewari, A., Thies, J., Mildenhall, B., Srinivasan, P., Tretschk, E., Wang, Y., Lassner, C., Sitzmann, V., Martin-Brualla, R., Lombardi, S., et al · 2021
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Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting
Xu, J., Wang, J., Long, M., et al · 2021
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A structured dictionary perspective on implicit neural representations
Yüce, G., Ortiz-Jiménez, G., Besbinar, B., and Frossard, P · 2021
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Informer: Beyond efficient transformer for long sequence time-series forecasting
Zhou, H., Zhang, S., Peng, J., Zhang, S., Li, J., Xiong, H., and Zhang, W · 2021
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N-hits: Neural hierarchical interpolation for time series forecasting
Challu, C., Olivares, K. G., Oreshkin, B. N., Garza, F., Mergenthaler, M., and Dubrawski, A · 2022
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Time-series anomaly detection with implicit neural representation
Jeong, K.-J. and Shin, Y.-M · 2022
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Non-stationary transformers: Exploring the stationarity in time series forecasting
Liu, Y., Wu, H., Wang, J., and Long, M · 2022
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Etsformer: Exponential smoothing transformers for time-series forecasting
Woo, G., Liu, C., Sahoo, D., Kumar, A., and Hoi, S · 2022
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Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Zhou, T., Ma, Z., Wen, Q., Wang, X., Sun, L., and Jin, R · 2022
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