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Here, we propose a general method for probabilistic time series forecasting.
Scoring rules for continuous probability distributions
Matheson, J. E. and Winkler, R. L · 1909
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Pattern Recognition and Machine Learning
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Mixture models: Inference and applications to clustering
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Long short-term memory
Hochreiter, S. and Schmidhuber, J · 1997
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Quantile Regression
Koenker, R · 2005
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Another look at measures of forecast accuracy
Hyndman, R. J. and Koehler, A. B · 2006
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Strictly proper scoring rules, prediction, and estimation
Gneiting, T. and Raftery, A. E · 2007
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Matplotlib: A 2D graphics environment
Hunter, J. D · 2007
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Verification tools for probabilistic forecasts of continuous hydrological variables
Laio, F. and Tamea, S · 2007
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Automatic time series forecasting: Theforecastpackage forR
Hyndman, R. J. and Khandakar, Y · 2008
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Generating sequences with recurrent neural networks
Graves, A · 2013
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Chung, J., Gulcehre, C., Cho, K., and Bengio, Y · 2014
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Deep learning in neural networks: An overview
Schmidhuber, J · 2014
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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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A distributional perspective on reinforcement learning
Bellemare, M. G., Dabney, W., and Munos, R · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L. u., and Polosukhin, I · 2017
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The M4 Competition: 100,000 time series and 61 forecasting methods
Makridakis, S., Spiliotis, E., and Assimakopoulos, V · 2019
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PyTorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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Fully Parameterized Quantile Function for Distributional Reinforcement Learning
Yang, D., Zhao, L., Lin, Z., Qin, T., Bian, J., and Liu, T.-Y · 2019
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GluonTS: Probabilistic and Neural Time Series Modeling in Python
Alexandrov, A., Benidis, K., Bohlke-Schneider, M., Flunkert, V., Gasthaus, J., Januschowski, T., Maddix, D. C., Rangapuram, S., Salinas, D., Schulz, J., Stella, L., Türkmen, A. C., and Wang, Y · 2020
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Array programming with NumPy
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del R’ıo, J. F., Wiebe, M., Peterson, P., G’erard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E · 2020
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Deep and confident prediction for time series at uber
Zhu, L. and Laptev, N · 2017
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Implicit Quantile Networks for Distributional Reinforcement Learning
Dabney, W., Ostrovski, G., Silver, D., and Munos, R · 2018
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Forecasting: Principles and practice
Hyndman, R. and Athanasopoulos, G · 2018
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Autoregressive quantile networks for generative modeling
Ostrovski, G., Dabney, W., and Munos, R · 2018
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Probabilistic Forecasting with Spline Quantile Function RNNs
Gasthaus, J., Benidis, K., Wang, Y., Rangapuram, S. S., Salinas, D., Flunkert, V., and Januschowski, T · 2019
Cited alongside, same era.
High-dimensional multivariate forecasting with low-rank Gaussian copula processes
Salinas, D., Bohlke-Schneider, M., Callot, L., Medico, R., and Gasthaus, J
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N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Oreshkin, B. N., Carpov, D., Chapados, N., and Bengio, Y · 2020
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pandas-dev/pandas: Pandas, February 2020
Pandas development team, T · 2020
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PytorchTS, 2021
Rasul, K · 2021
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Accuracy measures: theoretical and practical concerns
Makridakis, S · 2070
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DeepAR: Probabilistic forecasting with autoregressive recurrent networks
Salinas, D., Flunkert, V., Gasthaus, J., and Januschowski, T · 2070
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