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We introduce Merlion, an open-source machine learning library for time series.
A method for constructing local monotone piecewise cubic interpolants
F. N. Fritsch and J. Butland · 1984
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Stl: A seasonal-trend decomposition
Robert B Cleveland, William S Cleveland, Jean E McRae, and Irma Terpenning · 1990
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Random decision forests
Tin Kam Ho · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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The theta model: a decomposition approach to forecasting
Vassilis Assimakopoulos and Konstantinos Nikolopoulos · 2000
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The m3-competition: results, conclusions and implications
Spyros Makridakis and Michele Hibon · 2000
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Topics in Optimal Transportation
Cédric Villani · 2003
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New Introduction to Multiple Time Series Analysis
Helmut Lütkepohl · 2005
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Characteristic-based clustering for time series data
Xiaozhe Wang, Kate Smith, and Rob Hyndman · 2006
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Automatic time series forecasting: the forecast package for r
Rob J Hyndman and Yeasmin Khandakar · 2008
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Isolation forest
Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou · 2008
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statsmodels: Econometric and statistical modeling with python
Skipper Seabold and Josef Perktold · 2010
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Autoencoders, unsupervised learning, and deep architectures
Pierre Baldi · 2012
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Auto-encoding variational bayes, 2013
Diederik P Kingma and Max Welling · 2013
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V. Le · 2014
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Evaluating real-time anomaly detection algorithms - the numenta anomaly benchmark
Alexander Lavin and Subutai Ahmad · 2015
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Robust random cut forest based anomaly detection on streams
Sudipto Guha, Nina Mishra, Gourav Roy, and Okke Schrijvers · 2016
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Swat: a water treatment testbed for research and training on ics security
Aditya P. Mathur and Nils Ole Tippenhauer · 2016
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Lightgbm: A highly efficient gradient boosting decision tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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pmdarima, 2017
Taylor Smith, Gary Foreman, Charles Drotar, Steven Hoelscher, Aaron Smith, Krishna Sunkara, and Christopher Siewert · 2017
Deep autoencoding gaussian mixture model for unsupervised anomaly detection
Bo Zong, Qi Song, Martin Renqiang Min, Wei Cheng, Cristian Lumezanu, Daeki Cho, and Haifeng Chen · 2018
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Aiops: real-world challenges and research innovations
Yingnong Dang, Qingwei Lin, and Peng Huang · 2019
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STUMPY: A Powerful and Scalable Python Library for Time Series Data Mining
Sean M. Law · 2019
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The m4 competition: 100,000 time series and 61 forecasting methods
Spyros Makridakis, Evangelos Spiliotis, and Vassilios Assimakopoulos · 2019
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Time-series anomaly detection service at microsoft
Hansheng Ren, Bixiong Xu, Yujing Wang, Chao Yi, Congrui Huang, Xiaoyu Kou, Tony Xing, Mao Yang, Jie Tong, and Qi Zhang · 2019
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Temporal pattern attention for multivariate time series forecasting
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Forecasting at scale
Sean J. Taylor and Benjamin Letham · 2017
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URL http://iops.ai/competition_detail/?competition_id=5
AIOps Challenge, 2018 · 2018
Cited alongside, same era.
URL https://www.kaggle.com/robikscube/hourly-energy-consumption/metadata
Hourly energy consumption, 2018 · 2018
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URL https://www.kaggle.com/city-of-seattle/seattle-burke-gilman-trail/metadata
Seattle burke-gilman trail, 2018 · 2018
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The ucr time series classification archive, October 2018
Hoang Anh Dau, Eamonn Keogh, Kaveh Kamgar, Chin-Chia Michael Yeh, Yan Zhu, Shaghayegh Gharghabi, Chotirat Ann Ratanamahatana, Yanping, Bing Hu, Nurjahan Begum, Anthony Bagnall, Abdullah Mueen, Gustavo Batista, and Hexagon-ML · 2018
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Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding
Kyle Hundman, Valentino Constantinou, Christopher Laporte, Ian Colwell, and Tom Söderström · 2018
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Shun-Yao Shih, Fan-Keng Sun, and Hung-yi Lee · 2019
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Robust anomaly detection for multivariate time series through stochastic recurrent neural network
Ya Su, Youjian Zhao, Chenhao Niu, Rong Liu, Wei Sun, and Dan Pei · 2019
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Alibi detect: Algorithms for outlier, adversarial and drift detection, 2019
Arnaud Van Looveren, Giovanni Vacanti, Janis Klaise, Alexandru Coca, and Oliver Cobb · 2019
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GluonTS: Probabilistic and Neural Time Series Modeling in Python
Alexander Alexandrov, Konstantinos Benidis, Michael Bohlke-Schneider, Valentin Flunkert, Jan Gasthaus, Tim Januschowski, Danielle C. Maddix, Syama Rangapuram, David Salinas, Jasper Schulz, Lorenzo Stella, Ali Caner Türkmen, and Yuyang Wang · 2020
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Usad: Unsupervised anomaly detection on multivariate time series
Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, and Maria A. Zuluaga · 2020
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Multi-scale exponential smoother, 2021
Rowan Cassius, Arun Kumar Jagota, and Aadyot Bhatnagar · 2021
Closest in time.
Greykite: a flexible, intuitive and fast forecasting library, 2021
Reza Hosseini, Albert Chen, Kaixu Yang, Sayan Patra, and Rachit Arora · 2021
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Kats, 2021
Xiaodong Jiang · 2021
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