Paper Citation Record · LEDGER

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification

As of 22 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2608.08207.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2608.08207 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T00:19:52.211167Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

verified exact1
verified fuzzy22
unresolved18
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e5ad3068-1edf-4724-85a8-636fa1547ed3 · outbound

This paper cites A comprehensive review on machine learning in healthcare industry: classification, restrictions, opportunities and challenges.Sensors, 23(9):4178,.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification A comprehensive review on machine learning in healthcare industry: classification, restrictions, opportunities and challenges.Sensors, 23(9):4178,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.989459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 974b771b-7c74-4701-aa16-66524ed7fa23 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Efficiently Modeling Long Sequences with Structured State Spaces

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.028434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceb16c51-ffdb-4b66-a4d7-9c55842fef40 · outbound

This paper cites Modeling long-and short-term temporal patterns with deep neural networks.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Modeling long-and short-term temporal patterns with deep neural networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.054867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc6add5d-1ca9-41e3-92c5-d46b1a4594c8 · outbound

This paper cites PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification PolyLoss: A Polynomial Expansion Perspective of Classification Loss Functions

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.059484Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8666d23-fe9c-41e2-9cb5-95402c5c6b39 · outbound

This paper cites Human activity recognition based on multienvironment sensor data.Information Fusion, 91:47– 63,.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Human activity recognition based on multienvironment sensor data.Information Fusion, 91:47– 63,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.840593Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 501fa1a6-196a-43a3-acf2-3e5c9113052e · outbound

This paper cites Focal loss for dense ob- ject detection.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Focal loss for dense ob- ject detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.822503Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9ce5a2ba-c91c-419d-b1ec-92b03dad645a · outbound

This paper cites Scinet: Time series modeling and forecasting with sample convo- lution and interaction.Advances in Neural Information Processing Systems, 35:5816–5828,.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Scinet: Time series modeling and forecasting with sample convo- lution and interaction.Advances in Neural Information Processing Systems, 35:5816–5828,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.079403Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10c7c7c1-9332-48c0-9a5a-907e1110482c · outbound

This paper cites Mod- erntcn: A modern pure convolution structure for general time series analysis.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Mod- erntcn: A modern pure convolution structure for general time series analysis

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.791108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c4c661cd-c52b-4eec-95c5-8c2df737ac98 · outbound

This paper cites Time series contrastive learning with information-aware augmentations.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Time series contrastive learning with information-aware augmentations

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.771263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d1344c03-a16a-43ec-b0ae-15f2f9cd64b7 · outbound

This paper cites Mptsnet: Integrating multiscale peri- odic local patterns and global dependencies for multivari- ate time series classification.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Mptsnet: Integrating multiscale peri- odic local patterns and global dependencies for multivari- ate time series classification

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.750648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 676c5415-5cee-4ab2-a2db-3c56d2ddb219 · outbound

This paper cites an unresolved cited work.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-12T00:19:52.732037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 6afeab8f-bdbe-4a92-ad50-48ce8941f4b1 · outbound

This paper cites Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.111243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e7c16c0-65ab-4519-abb5-46e05c8e6e87 · outbound

This paper cites FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series Classification

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-12T00:19:52.353864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 972647d6-676a-4aa4-a2ef-97a0a16576f4 · outbound

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

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.127834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5e3c36a3-bc2b-4b5b-bafa-5d87c13f4828 · outbound

This paper cites Time series classification from scratch with deep neural networks: A strong baseline.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Time series classification from scratch with deep neural networks: A strong baseline

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.694236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation e1ea0cdf-4a8c-4085-ab57-165d7155f0f1 · outbound

This paper cites Micn: Multi-scale local and global context modeling for long- term series forecasting.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Micn: Multi-scale local and global context modeling for long- term series forecasting

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.676448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a97c65a5-0ca3-4570-9e1c-79b7e11bd2dc · outbound

This paper cites Transformers in Time Series: A Survey.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Transformers in Time Series: A Survey

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.147409Z

Source-reported events for the cited work

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Observation 5b83770e-bc1f-485e-b660-da6c15533163 · outbound

This paper cites Autoformer: Decomposition transform- ers with auto-correlation for long-term series forecast- ing.Advances in neural information processing systems, 34:22419–22430,.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Autoformer: Decomposition transform- ers with auto-correlation for long-term series forecast- ing.Advances in neural information processing systems, 34:22419–22430,

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.153032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af355132-501f-4e13-a609-64f3fc8f0dd3 · outbound

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

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.157940Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04ca5381-c0a1-4973-8ac7-3b0d3d454fcb · outbound

This paper cites Flowformer: Linearizing Transformers with Conservation Flows.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Flowformer: Linearizing Transformers with Conservation Flows

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.163717Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2676a74d-a858-4319-aecc-ae2194d0a9b2 · outbound

This paper cites Frequency-domain mlps are more effective learners in time series forecasting.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Frequency-domain mlps are more effective learners in time series forecasting

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.643922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a4cea7bd-d1ec-4c7e-94b2-a815be78e940 · outbound

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

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Ts2vec: Towards universal representation of time series

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.611429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation fa2d6089-460b-4b7b-9c39-1b33f7c6fc3a · outbound

This paper cites Are transformers effective for time series fore- casting? InProceedings of the AAAI conference on artifi- cial intelligence, volume 37, pages 11121–11128,.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Are transformers effective for time series fore- casting? InProceedings of the AAAI conference on artifi- cial intelligence, volume 37, pages 11121–11128,

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.179665Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5165e194-4d6a-4fa8-908f-8a30882c823f · outbound

This paper cites Crossformer: Transformer utilizing cross-dimension de- pendency for multivariate time series forecasting.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Crossformer: Transformer utilizing cross-dimension de- pendency for multivariate time series forecasting

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.566376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8d6c16fe-af2c-4e64-9a58-c27a93929bfe · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification mixup: Beyond Empirical Risk Minimization

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.188700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92d5d28c-2fbf-4a5c-acae-c5744b97a4ac · outbound

This paper cites Tapnet: Multivariate time se- ries classification with attentional prototypical network.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Tapnet: Multivariate time se- ries classification with attentional prototypical network

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.539548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 88f11a96-4197-4f73-8328-63b3ef087856 · outbound

This paper cites Time series classification using multi-channels deep convolutional neural networks.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Time series classification using multi-channels deep convolutional neural networks

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.522072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 75fecbe0-bf46-4cc6-8e69-442e077b4377 · outbound

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

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.206424Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e723fb29-80d8-473d-85f0-e4934dad8b22 · outbound

This paper cites Svp-t: A shape-level variable-position transformer for multivariate time series classification.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Svp-t: A shape-level variable-position transformer for multivariate time series classification

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.477203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 4b365238-9eff-4dd1-992e-f561288a8e19 · outbound

This paper cites Auto tcl: Automated time series contrastive learning with adaptive augmentations.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Auto tcl: Automated time series contrastive learning with adaptive augmentations

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.505136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation d37473d2-3073-4217-bbfe-273789b386c8 · outbound

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

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Deep learning for time series classi- fication: a review.Data mining and knowledge discovery, 33(4):917–963,

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.915698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8bb44810-dacd-495c-bc9a-2f058d8e96a9 · outbound

This paper cites Time-series pattern recognition in smart manufacturing systems: A literature review and ontology.Journal of Manufacturing Systems, 69:208–241,.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Time-series pattern recognition in smart manufacturing systems: A literature review and ontology.Journal of Manufacturing Systems, 69:208–241,

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.953272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 7afeadfa-141b-432c-9a98-f0b659a4c977 · outbound

This paper cites Multivariate lstm-fcns for time series classification.Neural networks, 116:237–245,.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Multivariate lstm-fcns for time series classification.Neural networks, 116:237–245,

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.879755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 0511a405-3be1-4d1e-83a0-a733131b4805 · outbound

This paper cites Msgnet: Learning multi- scale inter-series correlations for multivariate time series forecasting.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Msgnet: Learning multi- scale inter-series correlations for multivariate time series forecasting

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.971501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c4ca4b78-45cd-423f-a5de-54832d18ccef · outbound

This paper cites Lstm fully convolu- tional networks for time series classification.IEEE access, 6:1662–1669,.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Lstm fully convolu- tional networks for time series classification.IEEE access, 6:1662–1669,

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.896886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f53efee4-8f26-4153-8c3c-f6a686e93009 · outbound

This paper cites What makes for good views for contrastive learning?Advances in neural information processing systems, 33:6827–6839,.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification What makes for good views for contrastive learning?Advances in neural information processing systems, 33:6827–6839,

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.713779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation a97a543d-1438-4fec-9c8d-3820ec6f660b · outbound

This paper cites Early clas- sification on multivariate time series.Neurocomputing, 149:777–787,.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Early clas- sification on multivariate time series.Neurocomputing, 149:777–787,

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T00:19:52.934306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 07e0fff7-2a91-4862-9c55-244296821af3 · outbound

This paper cites Shapenet: A shapelet-neural network ap- proach for multivariate time series classification.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Shapenet: A shapelet-neural network ap- proach for multivariate time series classification

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.065176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c904dafe-d611-456c-9e65-e422bf46bb42 · outbound

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

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification The UEA multivariate time series classification archive, 2018

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.005872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d4d18c6e-3d39-4e87-ba49-f01b098800f6 · outbound

This paper cites Multi-Scale Convolutional Neural Networks for Time Series Classification.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification Multi-Scale Convolutional Neural Networks for Time Series Classification

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.016677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8138485e-6233-48dd-8fd7-5f38730f667a · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

FreSH: Frequency-Segmented Hierarchical Multi-Expert Framework for Multivariate Time Series Classification A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-12T00:19:52.100980Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.