PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series | Paper Citation Record

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This paper cites A public domain dataset for human activity recognition using smartphones.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series A public domain dataset for human activity recognition using smartphones

Reference 1

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Observation 55562235-af3d-4b9f-8f23-d964417df40a · outbound

This paper cites The UEA multivariate time series classification archive, 2018.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series The UEA multivariate time series classification archive, 2018

Reference 2

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Observation 5e283238-89e5-4aaf-8dd3-fab0c84ee0da · outbound

This paper cites Deep clustering for unsupervised learning of visual features.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Deep clustering for unsupervised learning of visual features

Reference 3

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Observation 7aaf0ba8-428b-4577-a629-cf3ae827effe · outbound

This paper cites A simple framework for contrastive learning of visual representations.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series A simple framework for contrastive learning of visual representations

Reference 4

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This paper cites Dtw-d: time series semi-supervised learning from a single example.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Dtw-d: time series semi-supervised learning from a single example

Reference 5

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This paper cites Time series forecasting for nonlinear and non-stationary processes: a review and comparative study.Iie Transactions, 47(10):1053–1071, 2015.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Time series forecasting for nonlinear and non-stationary processes: a review and comparative study.Iie Transactions, 47(10):1053–1071, 2015

Reference 6

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This paper cites Anomaly detection for iot time-series data: A survey.IEEE Internet of Things Journal, 7(7):6481–6494, 2019.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Anomaly detection for iot time-series data: A survey.IEEE Internet of Things Journal, 7(7):6481–6494, 2019

Reference 7

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Observation 4f9afc5a-e5b6-4da3-97ad-9ff7363d6d70 · outbound

This paper cites An unsupervised approach for periodic source detection in time series.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series An unsupervised approach for periodic source detection in time series

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:54.044082Z

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Observation 55b70c64-63a3-448e-ae80-c4f53438ddcd · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 9

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Observation 6364eeb2-c9b4-4e37-be7a-963df7c04da1 · outbound

This paper cites Time-Series Representation Learning via Temporal and Contextual Contrasting.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Time-Series Representation Learning via Temporal and Contextual Contrasting

Reference 10

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Observation c0ef6517-d09b-42dd-8693-9a5edb77fbd3 · outbound

This paper cites T-rep: Representation learning for time series using time-embeddings.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series T-rep: Representation learning for time series using time-embeddings

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:54.028630Z

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Observation 9f85def3-6d67-4ce5-b1a2-dc3dc75818b9 · outbound

This paper cites Unsupervised scalable representation learning for multivariate time series.Advances in neural information processing systems, 32, 2019.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Unsupervised scalable representation learning for multivariate time series.Advances in neural information processing systems, 32, 2019

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:54.019597Z

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Observation 2efcba10-51a2-4232-a961-79b85272096d · outbound

This paper cites Unsupervised Representation Learning by Predicting Image Rotations.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Unsupervised Representation Learning by Predicting Image Rotations

Reference 13

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Observation d6d6e4db-690c-4344-b5af-824287d12af6 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Momentum contrast for unsupervised visual representation learning

Reference 14

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This paper cites Deep learning for time series classification: a review.Data mining and knowledge discovery, 33(4):917–963, 2019.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Deep learning for time series classification: a review.Data mining and knowledge discovery, 33(4):917–963, 2019

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:54.004502Z

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Observation a71fea32-b8f1-4295-a835-d6b545a493fd · outbound

This paper cites Towards enhancing time series contrastive learning: A dynamic bad pair mining approach.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Towards enhancing time series contrastive learning: A dynamic bad pair mining approach

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:53.994913Z

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Observation 1ed7bd7f-d18b-426c-a2be-e185ef1da2ec · outbound

This paper cites Soft contrastive learning for time series.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Soft contrastive learning for time series

Reference 17

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Observation 36b6c6d3-810d-4c3a-a40d-8c63817c633d · outbound

This paper cites Time-series forecasting with deep learning: a survey.Philosophical Transactions of the Royal Society A, 379(2194):20200209, 2021.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Time-series forecasting with deep learning: a survey.Philosophical Transactions of the Royal Society A, 379(2194):20200209, 2021

Reference 18

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Observation ee28c35c-b7fa-4eb9-8168-8f86e6e12bb5 · outbound

This paper cites Self-supervised learning: Generative or contrastive.IEEE transactions on knowledge and data engineering, 35(1):857–876, 2021.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Self-supervised learning: Generative or contrastive.IEEE transactions on knowledge and data engineering, 35(1):857–876, 2021

Reference 19

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Observation 5b8fb12d-f34a-41ac-9cfd-9f0e88b421be · outbound

This paper cites Non-stationary transformers: Exploring the stationarity in time series forecasting.Advances in neural information processing systems, 35:9881–9893, 2022.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Non-stationary transformers: Exploring the stationarity in time series forecasting.Advances in neural information processing systems, 35:9881–9893, 2022

Reference 20

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Observation aba3fff5-3c29-4c84-aa64-e46aea40cb81 · outbound

This paper cites John Wiley & Sons, 2015.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series John Wiley & Sons, 2015

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:53.961334Z

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Observation 2a34deac-578f-456b-8f53-b8275ef8c6e9 · outbound

This paper cites Application of wavelet techniques in ecg signal processing: an overview.International Journal of Engineering Science and Technology (IJEST), 3(10):7432–7443, 2011.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Application of wavelet techniques in ecg signal processing: an overview.International Journal of Engineering Science and Technology (IJEST), 3(10):7432–7443, 2011

Reference 22

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This paper cites A review on iot healthcare monitoring applications and a vision for transforming sensor data into real-time clinical feedback.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series A review on iot healthcare monitoring applications and a vision for transforming sensor data into real-time clinical feedback

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:53.943262Z

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Observation b55848fc-7b8a-44c7-8c78-f111aadf42ac · outbound

This paper cites Unsupervised learning of visual representations by solving jigsaw puzzles.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Unsupervised learning of visual representations by solving jigsaw puzzles

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:53.933615Z

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Observation c4ae09c2-6de2-4891-9135-2aad12f60e2e · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Representation Learning with Contrastive Predictive Coding

Reference 25

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This paper cites Time-series anomaly detection service at microsoft.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Time-series anomaly detection service at microsoft

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:53.923709Z

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This paper cites A primer on contrastive pretraining in language processing: Methods, lessons learned, and perspectives.ACM Computing Surveys, 55(10):1–17, 2023.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series A primer on contrastive pretraining in language processing: Methods, lessons learned, and perspectives.ACM Computing Surveys, 55(10):1–17, 2023

Reference 27

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This paper cites Wavelet transform application for/in non-stationary time-series analysis: A review.Applied sciences, 9(7):1345, 2019.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Wavelet transform application for/in non-stationary time-series analysis: A review.Applied sciences, 9(7):1345, 2019

Reference 28

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Observation f2ceff33-9622-42a3-b29a-451d2e6b1d3d · outbound

This paper cites Clustering longitudinal clinical marker trajectories from electronic health data: Applications to phenotyping and endotype discovery.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Clustering longitudinal clinical marker trajectories from electronic health data: Applications to phenotyping and endotype discovery

Reference 29

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Observation 9b4bbc3b-2f5f-47eb-87a8-5a7f162333f3 · outbound

This paper cites Learning tasks for multitask learning: Heterogenous patient populations in the icu.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Learning tasks for multitask learning: Heterogenous patient populations in the icu

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:53.885195Z

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Observation c541daf5-8783-47d5-acac-0a4a2a54df0b · outbound

This paper cites Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding

Reference 31

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Observation cdb606c8-bd26-46ff-89d3-79638dd581b1 · outbound

This paper cites Universal Time-Series Representation Learning: A Survey.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Universal Time-Series Representation Learning: A Survey

Reference 32

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Observation 628188f3-1412-42c1-81b5-5bac8b019255 · outbound

This paper cites Ptb-xl, a large publicly available electrocardiography dataset.Scientific data, 7(1):1–15, 2020.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Ptb-xl, a large publicly available electrocardiography dataset.Scientific data, 7(1):1–15, 2020

Reference 33

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Observation 9288a770-b619-47d3-af8d-2bb30b21f873 · outbound

This paper cites Time2state: An unsupervised framework for inferring the latent states in time series data.Proceedings of the ACM on Management of Data, 1(1):1–18, 2023.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Time2state: An unsupervised framework for inferring the latent states in time series data.Proceedings of the ACM on Management of Data, 1(1):1–18, 2023

Reference 34

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This paper cites Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Chaos is a Ladder: A New Theoretical Understanding of Contrastive Learning via Augmentation Overlap

Reference 35

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Observation 27b62fca-2e00-417f-9e91-94cf03e71891 · outbound

This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 36

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This paper cites Simper: Simple self-supervised learning of periodic targets.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Simper: Simple self-supervised learning of periodic targets

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:53.857972Z

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Observation 3036f835-6b8d-44da-8cd1-95ee488611a2 · outbound

This paper cites Ts2vec: Towards universal representation of time series.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Ts2vec: Towards universal representation of time series

Reference 38

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This paper cites Deep learning for time series anomaly detection: A survey.ACM Computing Surveys, 57(1):1–42, 2024.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Deep learning for time series anomaly detection: A survey.ACM Computing Surveys, 57(1):1–42, 2024

Reference 39

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Observation e1977cb0-1fbb-4258-ba84-4d7624e2ce65 · outbound

This paper cites A transformer- based framework for multivariate time series representation learning.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series A transformer- based framework for multivariate time series representation learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:53.835827Z

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Observation 9a289f5e-3589-4d1c-bf19-840451c7516b · outbound

This paper cites Self-supervised learning for time series analysis: Taxonomy, progress, and prospects.IEEE transactions on pattern analysis and machine intelligence, 46(10):6775–6794, 2024.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Self-supervised learning for time series analysis: Taxonomy, progress, and prospects.IEEE transactions on pattern analysis and machine intelligence, 46(10):6775–6794, 2024

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:53.826541Z

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Observation 95176f13-8017-4fb0-82ba-180db17a99c3 · outbound

This paper cites Self-supervised contrastive pre-training for time series via time-frequency consistency.Advances in neural information processing systems, 35:3988–4003, 2022.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Self-supervised contrastive pre-training for time series via time-frequency consistency.Advances in neural information processing systems, 35:3988–4003, 2022

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:29:53.817122Z

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Observation 02c8a817-831e-47ab-af01-4c5de8fa3504 · outbound

This paper cites Springer, 2018.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Springer, 2018

Reference 43

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Observation 93e36ed9-7deb-4853-b979-4f850d6f00e2 · outbound

This paper cites Parametric Augmentation for Time Series Contrastive Learning.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Parametric Augmentation for Time Series Contrastive Learning

Reference 44

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This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting.

PLanTS: Periodicity-aware Latent-state Representation Learning for Multivariate Time Series Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 45

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