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We present sktime -- a new scikit-learn compatible Python library with a unified interface for machine learning with time series.
WEKA: a machine learning workbench
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J. Lin, E. Keogh, L. Wei, and S. Lonardi · 2007
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Badi H. Baltagi · 2008
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Alina Beygelzimer, John Langford, and Bianca Zadrozny · 2008
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Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten · 2009
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Statsmodels: Econometric and Statistical Modeling with Python Quantitative histology of aorta View project Statsmodels: Econometric and Statistical Modeling with Python
Josef Perktold and Skipper Seabold · 2010
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Econometric analysis of cross section and panel data
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Cython: The best of both worlds
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pandas: a Foundational Python Library for Data Analysis and Statistics
Wes McKinney · 2011
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Édouard Duchesnay · 2011
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The NumPy Array: A Structure for Efficient Numerical Computation
Stéfan van der Walt, S Chris Colbert, and Gaël Varoquaux · 2011
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Supervised sequence labelling
Alex Graves · 2012
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Rotation-invariant similarity in time series using bag-of-patterns representation
J. Lin, R. Khade, and Y. Li · 2012
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SFA: a symbolic Fourier approximation and index for similarity search in high dimensional datasets
P. Schäfer and M. Högqvist · 2012
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Machine Learning Strategies for Time Series Forecasting
Gianluca Bontempi, Souhaib Ben Taieb, and Yann-Aël Le Borgne · 2013
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API design for machine learning software: experiences from the scikit-learn project
Lars Buitinck, Gilles Louppe, Mathieu Blondel, Fabian Pedregosa, Andreas C Müller, Olivier Grisel, Vlad Niculae, Peter Prettenhofer, Alexandre Gramfort, Jaques Grobler, Robert Layton, Jake Vanderplas, Arnaud Joly, Brian Holt, and Gaël Varoquaux · 2013
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A time series forest for classification and feature extraction
Houtao Deng, George Runger, Eugene Tuv, and Martyanov Vladimir · 2013
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hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction
Ben D Fulcher and Nick S Jones · 2017
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xarray: N-D labeled Arrays and Datasets in Python
Stephan Hoyer and Joseph J. Hamman · 2017
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tslearn: A machine learning toolkit dedicated to time-series data, 2017
Romain Tavenard · 2017
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A note on the validity of cross-validation for evaluating autoregressive time series prediction
Christoph Bergmeir, Rob J Hyndman, and Bonsoo Koo · 2018
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Seglearn: a python package for learning sequences and time series
David M Burns and Cari M Whyne · 2018
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Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)
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Analysis of longitudinal data
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Alina Beygelzimer, Hal Daumé, John Langford, and Paul Mineiro · 2015
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Time series analysis: forecasting and control
George E. P. Box, Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung · 2015
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Deep feature synthesis: Towards automating data science endeavors
James Max Kanter and Kalyan Veeramachaneni · 2015
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Forests of randomized shapelet trees
I. Karlsson, Papapetrou P, and H. Boström · 2015
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Time series classification with ensembles of elastic distance measures
J. Lines and A. Bagnall · 2015
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Maximilian Christ, Nils Braun, Julius Neuffer, and Andreas W. Kempa-Liehr · 2018
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xpandas: Python data containers for structured types and structured machine learning tasks
Vitaly Davydov and Franz J Király · 2018
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pysf: Supervised forecasting of sequential data in Python, 2018
Ahmed Guecioueur · 2018
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Forecasting: principles and practice
Rob J Hyndman and George Athanasopoulos · 2018
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Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles
J. Lines, S. Taylor, and A. Bagnall · 2018
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Statistical and machine learning forecasting methods: Concerns and ways forward
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2018
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Forecasting at scale
Sean J Taylor and Benjamin Letham · 2018
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A review on distance based time series classification
A. Abanda, U. Mori, and J. Lozano · 2019
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Gluonts: Probabilistic time series models in python
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, et al · 2019
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Anthony Bagnall, Markus Löning, Matthew Middlehurst, and George Oastler · 2019
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Deep learning for time series classification: a review
H. Fawaz, G. Forestier, J. Weber, L. Idoumghar, and P. Muller · 2019
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Machine learning automation toolbox (mlaut)
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