sktime: A Unified Interface for Machine Learning with Time Series M Löning, A Bagnall, S Ganesh, V Kazakov, J Lines, FJ Király Workshop on Systems for ML at NeurIPS 2019, 2019 | 261 | 2019 |
Can degrowth overcome the leakage problem of unilateral climate policy? M Larch, M Löning, J Wanner Ecological economics 152, 118-130, 2018 | 10 | 2018 |
Designing Machine Learning Toolboxes: Concepts, Principles and Patterns FJ Király, M Löning, A Blaom, A Guecioueur, R Sonabend arXiv preprint arXiv:2101.04938, 2021 | 9 | 2021 |
A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency A Bagnall, F Király, M Löning, M Middlehurst, G Oastler arXiv preprint arXiv:1909.05738, 2019 | 9 | 2019 |
Forecasting with sktime: Designing sktime's new forecasting api and applying it to replicate and extend the m4 study M Löning, F Király arXiv preprint arXiv:2005.08067, 2020 | 8 | 2020 |
A unified interface for machine learning with time series M Löning, A Bagnall, S Ganesh, V Kazakov, J Lines, FJ Király arXiv preprint arXiv:1909.07872, 2019 | 7 | 2019 |
A unified interface for machine learning with time series. ArXiv e-prints M Löning, A Bagnall, S Ganesh, V Kazakov, J Lines, FJ Király arXiv preprint arXiv:1909.07872, 2019 | 6 | 2019 |
Machine Learning with Time Series: A Taxonomy of Learning Tasks, Development of a Unified Framework, and Comparative Benchmarking of Algorithms M Löning UCL (University College London), 2021 | | 2021 |