time-series-2

Clustering Multivariate Time Series

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs

A. Gholami, K. Keutzer, G. Biros•Tue Feb 26 2019

ANODE is proposed, an Adjoint based Neural ODE framework which avoids the numerical instability related problems, and provides unconditionally accurate gradients, and discusses a memory efficient algorithm which can further reduce this footprint with a trade-off of additional computational cost.

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Autoencoder-Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm

Jonas Köhne, Lars Henning, C. Gühmann•Thu Sep 08 2022

An algorithm for the detection of change-points and the identification of the corresponding subsequences in transient multivariate time-series data (MTSD) is introduced that uses a recurrent neural network (RNN) based Autoencoder (AE) which is iteratively trained on incoming data.

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