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We extend conformal inference to general settings that allow for time series data.
The Design of Experiments
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Introduction to strong mixing conditions
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Conformal prediction with neural networks
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Network traffic demand prediction with confidence
Dashevskiy, M. and Luo, Z. (2008) · 2008
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Simultaneous analysis of lasso and dantzig selector
Bickel, P. J., Ritov, Y., and Tsybakov, A. B. (2009) · 2009
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Time series prediction with performance guarantee
Dashevskiy, M. and Luo, Z. (2011) · 2011
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Time series: theory and methods
Brockwell, P. J. and Davis, R. A. (2013) · 2013
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Distribution-free prediction sets
Conformal Prediction for Reliable Machine Learning
Balasubramanian, V. N., Ho, S.-S., and Vovk, V. (2014) · 2014
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Efficiency of conformalized ridge regression
Burnaev, E. and Vovk, V. (2014) · 2014
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Distribution-free prediction bands for non-parametric regression
Lei, J. and Wasserman, L. (2014) · 2014
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A conformal prediction approach to explore functional data
Lei, J., Rinaldo, A., and Wasserman, L. (2015) · 2015
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hdm: High-dimensional metrics
Chernozhukov, V., Hansen, C., and Spindler, M. (2016) · 2016
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An exact and robust conformal inference method for counterfactual and synthetic controls
Chernozhukov, V., Wüthrich, K., and Zhu, Y. (2017) · 2017
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Lei, J., Robins, J., and Wasserman, L. (2013) · 2013
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Conditional validity of inductive conformal predictors
Vovk, V. (2013) · 2013
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Distribution-free predictive inference for regression
Lei, J., G’Sell, M., Rinaldo, A., Tibshirani, R. J., and Wasserman, L. (2017) · 2017
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Asymptotic Theory of Weakly Dependent Random Processes
Rio, E. (2017) · 2017
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