GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series | Paper Citation Record

Observation 8a24f390-c1b8-493c-bc5c-a696a1e7d748 · outbound

This paper cites Geodynamics,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Geodynamics,

Reference 1

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Observation 20993f48-590c-4f52-911e-a84901452b3e · outbound

This paper cites Harnessing the GPS data explosion for interdisciplinary science,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Harnessing the GPS data explosion for interdisciplinary science,

Reference 2

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Observation 39d8f7d2-83a0-4ee6-ba14-e4607a5690bd · outbound

This paper cites Discontinuity detection in GNSS station coordinate time series using machine learning,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Discontinuity detection in GNSS station coordinate time series using machine learning,

Reference 3

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Observation 3dafeb1a-8f82-496d-8c76-97fad0477a7e · outbound

This paper cites An improved VMD-LSTM model for time-varying GNSS time series prediction with temporally correlated noise,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series An improved VMD-LSTM model for time-varying GNSS time series prediction with temporally correlated noise,

Reference 4

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Observation d0ed542c-d27f-45fc-8d70-fd662d2e1eb8 · outbound

This paper cites Multi-station deep learning on geodetic time series detects slow slip events in cascadia,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Multi-station deep learning on geodetic time series detects slow slip events in cascadia,

Reference 5

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Observation 01f62d51-ad2d-4dfd-94ae-5b2d8c3c7dac · outbound

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

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series BERT: Pre- training of deep bidirectional transformers for language understanding,

Reference 6

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Observation 68c828e8-20da-4afb-9377-760279e7eff5 · outbound

This paper cites Masked autoencoders are scalable vision learners,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Masked autoencoders are scalable vision learners,

Reference 7

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Observation 4fa1420c-78c3-4262-aece-be89f7c28652 · outbound

This paper cites Masked Autoencoders Are Scalable Vision Learners.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Masked Autoencoders Are Scalable Vision Learners

Reference 8

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Observation a74b19e7-f9cd-4897-bcb7-922ba8cb520b · outbound

This paper cites wav2vec 2.0: A framework for self-supervised learning of speech representations,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series wav2vec 2.0: A framework for self-supervised learning of speech representations,

Reference 9

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Observation 41f0dfc0-a4df-4d3d-9469-52e3faf05d9b · outbound

This paper cites SeisLM: a Foundation Model for Seismic Waveforms.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series SeisLM: a Foundation Model for Seismic Waveforms

Reference 10

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Observation 902ad608-b961-4e27-8c40-eec87183ee3c · outbound

This paper cites Beit: Bert pre-training of image transformers,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Beit: Bert pre-training of image transformers,

Reference 11

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Observation deb3d705-342c-4ccd-8392-34f599e6b765 · outbound

This paper cites Feature guided masked autoencoder for self-supervised learning in remote sens- ing,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Feature guided masked autoencoder for self-supervised learning in remote sens- ing,

Reference 12

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Observation 5c6910e4-bc2a-4fcc-a13a-c7de1adf797f · outbound

This paper cites HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series HuBERT: Self-supervised speech representation learning by masked prediction of hidden units,

Reference 13

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Observation 1fe5bdf0-71a2-43bc-b673-093cbc9bc0eb · outbound

This paper cites WavLM: Large-scale self-supervised pre-training for full stack speech processing,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series WavLM: Large-scale self-supervised pre-training for full stack speech processing,

Reference 14

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Observation d994a704-e5c3-43a6-a610-b6844fd3858f · outbound

This paper cites TS2Vec: Towards universal representation of time series,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series TS2Vec: Towards universal representation of time series,

Reference 15

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Observation e16ef245-a619-49bb-a408-5036d9d92e3e · outbound

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

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series A transformer-based framework for multivariate time series representation learning,

Reference 16

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Observation 99e23c8a-6bc3-4095-a440-9e48ea9b1be5 · outbound

This paper cites Vector quantization pretraining for EEG time series with random projection and phase alignment,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Vector quantization pretraining for EEG time series with random projection and phase alignment,

Reference 17

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Observation 59b0615e-55e0-4cea-a8ef-7a1628358ab2 · outbound

This paper cites Effective self-supervised transformers for sparse time series data,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Effective self-supervised transformers for sparse time series data,

Reference 18

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Observation 0981015e-7033-4b74-8912-cabd06b53e7d · outbound

This paper cites Self-supervised spatio-temporal representation learning of satellite image time series,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Self-supervised spatio-temporal representation learning of satellite image time series,

Reference 19

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Observation 26cafcd8-6a27-4f91-916c-cfe84b58be3a · outbound

This paper cites Chronos: Learning the Language of Time Series.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Chronos: Learning the Language of Time Series

Reference 20

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Observation 1c6c710c-7dcf-432e-ac70-76884994d26b · outbound

This paper cites A decoder-only foundation model for time-series forecasting.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series A decoder-only foundation model for time-series forecasting

Reference 21

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Observation e9507ac3-ba9b-4c00-ad28-bfcdf9813204 · outbound

This paper cites TimeGPT-1.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series TimeGPT-1

Reference 22

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Observation 970cb9cb-b2df-43f2-8b75-16f9ab9b6243 · outbound

This paper cites A foundation model for the earth system,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series A foundation model for the earth system,

Reference 23

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Observation ea0cd1e3-f317-438a-a12d-cb8a8e3dc028 · outbound

This paper cites Integrating GNSS- derived atmospheric delays into large weather foundation models,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Integrating GNSS- derived atmospheric delays into large weather foundation models,

Reference 24

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Observation 35f385cd-32c0-4ace-8afd-6e80b2e37b6f · outbound

This paper cites Earth system foundation model (esfm): A unified framework for heterogeneous data integration and forecasting,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Earth system foundation model (esfm): A unified framework for heterogeneous data integration and forecasting,

Reference 25

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Observation 65b977aa-0d90-4bd2-8866-fae1dfd274fb · outbound

This paper cites Alphaearth foundations: An embedding field model for accurate and efficient global mapping from sparse label data,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Alphaearth foundations: An embedding field model for accurate and efficient global mapping from sparse label data,

Reference 26

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Observation 8eed8730-8979-4018-bd16-0887473f4066 · outbound

This paper cites Denoising daily displacement GNSS time series using deep neural networks in a near real-time framing: a single-station method,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Denoising daily displacement GNSS time series using deep neural networks in a near real-time framing: a single-station method,

Reference 27

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Observation 61b63d39-bcc3-436b-8f72-c3c36c645701 · outbound

This paper cites Cascadia daily GNSS time series denoising: Graph neural network and stack filtering,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Cascadia daily GNSS time series denoising: Graph neural network and stack filtering,

Reference 28

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Observation 7ca3a266-8208-42ad-bd4c-f1899790121d · outbound

This paper cites Modeling of residual GNSS station motions through meteorological data in a machine learning approach,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Modeling of residual GNSS station motions through meteorological data in a machine learning approach,

Reference 29

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Observation 6d3ce037-4435-4ed2-ae39-ef5e98e2510a · outbound

This paper cites Correction models for GNSS displacements in europe based on en- vironmental variables and XGBoost,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Correction models for GNSS displacements in europe based on en- vironmental variables and XGBoost,

Reference 30

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Observation 755bfef9-bedc-4b8b-bee9-7a71c27ae350 · outbound

This paper cites Modelling of GNSS station position time series using deep learning approaches,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Modelling of GNSS station position time series using deep learning approaches,

Reference 31

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Observation 9ce765fd-56e4-426f-9fb4-421624b1dd9a · outbound

This paper cites Kiani Shahvandi and B.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Kiani Shahvandi and B

Reference 32

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Observation cbd19192-7ebe-4018-97f5-3554b2a9b534 · outbound

This paper cites The effect of coloured noise on the uncertainties of rates estimated from geodetic time series,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series The effect of coloured noise on the uncertainties of rates estimated from geodetic time series,

Reference 33

Resolution: unresolved; no resolver link, observed 2026-06-27T20:08:16.828717Z

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Observation 15d5a09c-a3ba-43f6-a623-c66d05b8c8b8 · outbound

This paper cites Anatomy of apparent seasonal variations from GPS-derived site position time series,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Anatomy of apparent seasonal variations from GPS-derived site position time series,

Reference 34

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Observation ff757268-2417-4828-9d2b-f491b70e2387 · outbound

This paper cites Itrf2020: an augmented reference frame refining the modeling of nonlinear station motions,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Itrf2020: an augmented reference frame refining the modeling of nonlinear station motions,

Reference 35

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Observation de3848b9-eff2-45ae-95a3-bfd80300a875 · outbound

This paper cites Wdowinski, Y.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Wdowinski, Y

Reference 36

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Observation 4abb52dd-5359-4ae4-a16c-1088d57a7360 · outbound

This paper cites Spa- tiotemporal filtering using principal component analysis and karhunen– lo`eve expansion approaches to GPS coordinate time series,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Spa- tiotemporal filtering using principal component analysis and karhunen– lo`eve expansion approaches to GPS coordinate time series,

Reference 37

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Observation 6cfd4f2c-e2ce-4ff1-a765-8189a88d89ed · outbound

This paper cites Noise-resilient GNSS coordinate time series prediction using A VMD-sLSTM-transformer hybrid model,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Noise-resilient GNSS coordinate time series prediction using A VMD-sLSTM-transformer hybrid model,

Reference 38

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Observation 8b2243c7-1ec7-4d2a-aacf-b9b0e61fe655 · outbound

This paper cites Generalized hampel filters,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Generalized hampel filters,

Reference 39

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Observation 40c0367b-e277-4bd7-b438-a6f9c6728ae3 · outbound

This paper cites The identification of multiple outliers,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series The identification of multiple outliers,

Reference 40

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Observation d1e2406e-c266-486d-a044-e6a243398c33 · outbound

This paper cites The jackknife and the bootstrap for general stationary observations,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series The jackknife and the bootstrap for general stationary observations,

Reference 41

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Observation 18d7e6f0-ad5e-4903-b276-a85c74bb9e48 · outbound

This paper cites A nearest neighbor bootstrap for resampling hydrologic time series,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series A nearest neighbor bootstrap for resampling hydrologic time series,

Reference 42

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Observation 4f7eb32d-0250-4988-96ef-ae09470f090e · outbound

This paper cites End-to-end object detection with transformers,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series End-to-end object detection with transformers,

Reference 43

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This paper cites Layer Normalization.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Layer Normalization

Reference 44

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Observation 53cdaf93-67ed-4ed6-bfae-2ef3d25aa348 · outbound

This paper cites Gaussian Error Linear Units (GELUs).

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Gaussian Error Linear Units (GELUs)

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-07-02T20:47:23.128870Z

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Observation 15f1d4bf-9b86-48a1-a65b-344d988e3008 · outbound

This paper cites Go- ing deeper with image transformers,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Go- ing deeper with image transformers,

Reference 46

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Observation f8dcd8f2-cd57-4429-b0bc-c61f589e4c10 · outbound

This paper cites FiLM: Visual reasoning with a general conditioning layer,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series FiLM: Visual reasoning with a general conditioning layer,

Reference 47

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Observation dcd04e32-5e47-4e1b-b01b-193c904bbab0 · outbound

This paper cites Categorical reparameterization with Gumbel-Softmax,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Categorical reparameterization with Gumbel-Softmax,

Reference 48

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Observation 11c198c0-b0fc-4541-a1ae-10772653bae9 · outbound

This paper cites Representation learning with contrastive predictive coding,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Representation learning with contrastive predictive coding,

Reference 49

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Observation 63dcc019-a518-4bcd-9cdc-da217387d811 · outbound

This paper cites LoRA: Low-Rank Adaptation of Large Language Models.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series LoRA: Low-Rank Adaptation of Large Language Models

Reference 50

Resolution: verified exact; local_arxiv, observed 2026-07-02T20:47:23.107007Z

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Observation 0bca7e45-c1c3-4290-9634-a37954bd3988 · outbound

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

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 51

Resolution: verified exact; local_arxiv, observed 2026-07-02T20:47:23.120859Z

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Observation c1c415dd-80cb-4ee8-9e6e-b38862088bb0 · outbound

This paper cites DAB-DETR: Dynamic anchor boxes are better queries for DETR,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series DAB-DETR: Dynamic anchor boxes are better queries for DETR,

Reference 52

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Observation be6aab9d-888b-4551-8cbf-d9f5d3f4f6fe · outbound

This paper cites Mallat,A Wavelet Tour of Signal Processing: The Sparse Way, 3rd ed.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series Mallat,A Wavelet Tour of Signal Processing: The Sparse Way, 3rd ed

Reference 53

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Observation 293c958f-9936-4bf1-9208-218d87c07fb6 · outbound

This paper cites XGBoost: A scalable tree boosting system,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series XGBoost: A scalable tree boosting system,

Reference 54

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Observation 5d78aa5e-c119-40af-87fa-4653c0addeeb · outbound

This paper cites On a test of whether one of two random variables is stochastically larger than the other,.

GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series On a test of whether one of two random variables is stochastically larger than the other,

Reference 55

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