Resources for working with time series and sequence data
A collection of resources for working with sequential and time series data
scikit-learn
like API.auto.arima
function.scikit-learn
compatible Python toolbox for learning with time series.statsmodels.tsa
contains model classes and functions that are useful for time series analysis.Libraries for working with dates and times.
tidyverse
toolkit to visualize, wrangle, and transform time series data.TS2Vec: Towards Universal Representation of Time Series, Zhihan Yue, Yujing Wang, Juanyong Duan, Tianmeng Yang, Congrui Huang, Yunhai Tong, Bixiong Xu, 2022
Conformal prediction interval for dynamic time-series, Chen Xu, Yao Xie, International Conference on Machine Learning 2021 (long presentation)
Deep learning for time series classification: a review, H. I. Fawaz, G. Forestier, J. Weber, L. Idoumghar, P-A. Muller, Data Mining and Knowledge Discovery 2019
Greedy Gaussian Segmentation of Multivariate Time Series, D. Hallac, P. Nystrup, and S. Boyd, Advances in Data Analysis and Classification, 13(3), 727β751, 2019.
U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging, Mathias Perslev, Michael Jensen, Sune Darkner, Poul JΓΈrgen Jennum, Christian Igel, NeurIPS, 2019.
A Better Alternative to Piecewise Linear Time Series Segmentation, Daniel Lemire, SIAM Data Mining, 2007.
Time-series Generative Adversarial Networks, Jinsung Yoon, Daniel Jarrett, Mihaela van der Schaar, NeurIPS, 2019.
Learning to Diagnose with LSTM Recurrent Neural Networks, Zachary C. Lipton, David C. Kale, Charles Elkan, Randall Wetzel, arXiv:1511.03677, 2015.
Coherence-based Label Propagation over Time Series for Accelerated Active Learning, Yooju Shin, Susik Yoon, Sundong Kim, Hwanjun Song, Jae-Gil Lee, Byung Suk Lee, ICLR, 2022.
tf.contrib.timeseries