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Forecasting with sktime: Designing sktime's New Forecasting API and Applying It to Replicate and Extend the M4 Study

, and . (2020)cite arxiv:2005.08067.

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Forecasting with sktime: Designing sktime's New Forecasting API and Applying It to Replicate and Extend the M4 Study, and . (2020)cite arxiv:2005.08067.MLJ: A Julia package for composable machine learning., , , , , and . J. Open Source Softw., 5 (55): 2704 (2020)Kernels for Sequentially Ordered Data., and . J. Mach. Learn. Res., (2019)Coherence and sufficient sampling densities for reconstruction in compressed sensing, and . CoRR, (2013)Dual-to-kernel learning with ideals., , and . CoRR, (2014)A Combinatorial Algebraic Approach for the Identifiability of Low-Rank Matrix Completion., and . ICML, icml.cc / Omnipress, (2012)Error-Minimizing Estimates and Universal Entry-Wise Error Bounds for Low-Rank Matrix Completion., and . NIPS, page 2364-2372. (2013)Source Separation With Side Information Based on Gaussian Mixture Models With Application in Art Investigation., , , and . IEEE Trans. Signal Process., (2020)Algebraic Reconstruction Bounds and Explicit Inversion for Phase Retrieval at the Identifiability Threshold., and . AISTATS, volume 33 of JMLR Workshop and Conference Proceedings, page 503-511. JMLR.org, (2014)Algebraic Geometric Comparison of Probability Distributions., , , , and . J. Mach. Learn. Res., (2012)