Semi-supervised time series classification

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

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Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

4 shown of 4 papers with code (6 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification
  13 Aug 2022 2 repositories listed Syntology ran 1 of 1 samples · 0 unverified · 1 pointer-only (licence)
  Specifically, we propose time-series specific weak and strong augmentations and use their views to learn robust temporal relations in the proposed temporal contrasting module, besides learning discriminative…
• SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligence
  15 Mar 2022 1 repository listed
  In this work, edge users collaborate to train a Hyper-network in the server, generating personalized autoencoders for each user.
• Semi-supervised Time Series Classification by Temporal Relation Prediction
  11 Jun 2021 1 repository listed
  Then, the temporal relation between those segments is predicted by SemiTime in a self-supervised manner.
• An Empirical Study of Graph-Based Approaches for Semi-Supervised Time Series Classification
  16 Apr 2021 1 repository listed
  Among the many time series learning tasks of great importance, we here focus on semi-supervised learning based on a graph representation of the data.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.