Paper Citation Record · LEDGER

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python

As of 21 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 0 inbound Pith citation observations for arXiv:2509.10560.

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

pith.paper-citation-record.v1
2509.10560 v1

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T20:50:44.991119Z

measured 26 of 26 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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

26 of 26 outbound references displayed

verified exact2
verified fuzzy16
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6c0274f5-24c7-4868-850a-812423064c9a · outbound

This paper cites Review of current GPS methodologies for producing accurate time series and their error sources,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Review of current GPS methodologies for producing accurate time series and their error sources,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.459315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 060586d8-8f78-4e04-8130-3934f0189a21 · outbound

This paper cites MIDAS robust trend estimator for accurate GPS station velocities without step detection,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python MIDAS robust trend estimator for accurate GPS station velocities without step detection,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.440940Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f28676c3-c7fc-4092-b1e4-a1d4ff55ff3c · outbound

This paper cites Interseismic strain accumulation and the earthquake potential on the southern San Andreas fault system,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Interseismic strain accumulation and the earthquake potential on the southern San Andreas fault system,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.423963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ec00035b-32f1-41e3-92af-bcd69c4d29e2 · outbound

This paper cites From geodetic imaging of seismic and aseismic fault slip to dynamic modeling of the seismic cycle,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python From geodetic imaging of seismic and aseismic fault slip to dynamic modeling of the seismic cycle,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.405692Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 97483d57-768b-4823-83ec-afd95364a7b0 · outbound

This paper cites Montillet and M.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Montillet and M

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.390321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4d5a0594-bbeb-4915-9b89-8fc3b901f80f · outbound

This paper cites The IGS contribution to ITRF2014,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python The IGS contribution to ITRF2014,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.375334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 85b2febe-d3f6-49bd-9132-e42fa8d52eb6 · outbound

This paper cites an unresolved cited work.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-04T20:50:45.360334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0613d369-0e9b-4c22-b3fe-5ec3c8f64b29 · outbound

This paper cites an unresolved cited work.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Unresolved cited work

Reference 8

Resolution: unresolved; raw_fallback, observed 2026-08-04T20:50:45.344566Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a1774403-dd44-442a-a556-ac966b6e60c4 · outbound

This paper cites Deep learning,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Deep learning,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T20:50:44.142805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f883e678-657e-4ce5-ba15-45aafd5c41a8 · outbound

This paper cites Goodfellow, Y.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Goodfellow, Y

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.318068Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 44188a43-eb3f-4757-84d1-8bb0c2155110 · outbound

This paper cites Modeling of regional GNSS network using adaptive boosting algorithm: a case study in the Xinjiang Uyghur Autonomous Region,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Modeling of regional GNSS network using adaptive boosting algorithm: a case study in the Xinjiang Uyghur Autonomous Region,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.303326Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d748608b-0f16-4ff8-b657-074ff9bec5f8 · outbound

This paper cites KAN: Kolmogorov-Arnold Networks.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python KAN: Kolmogorov-Arnold Networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T20:50:44.270376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ecc6cbba-231b-433c-a83d-515fecd651a8 · outbound

This paper cites TimeGNN: temporal dynamic graph learning for time series forecasting,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python TimeGNN: temporal dynamic graph learning for time series forecasting,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.285082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2d286818-f4ee-409d-810a-714e71539deb · outbound

This paper cites An improved ICEEMDAN- MPA-GRU model for GNSS height time series prediction with weighted quality evaluation index,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python An improved ICEEMDAN- MPA-GRU model for GNSS height time series prediction with weighted quality evaluation index,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.269438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8506f8cb-b3cb-4838-af89-8147ee6a4df0 · outbound

This paper cites Detection of data drift and outliers affecting machine learning model performance over time.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Detection of data drift and outliers affecting machine learning model performance over time

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-04T20:50:45.117541Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0ce30b9a-8608-4806-bd4e-7f581fa64582 · outbound

This paper cites Seo,A Review and Comparison of Methods for Detecting Outliers in Univariate Data Sets.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Seo,A Review and Comparison of Methods for Detecting Outliers in Univariate Data Sets

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.252618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c2a82a15-141a-4d46-a00d-39ae59a17a55 · outbound

This paper cites Piecewise deterministic Markov process for condition-based maintenance models—application to critical infrastructures with discrete-state deterioration,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Piecewise deterministic Markov process for condition-based maintenance models—application to critical infrastructures with discrete-state deterioration,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.235760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3092d447-a426-48dd-b4fc-f94b0b077bd2 · outbound

This paper cites Stability analysis of distributed Kalman filtering algorithm for stochastic regression model,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Stability analysis of distributed Kalman filtering algorithm for stochastic regression model,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.217771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cb3fac85-e753-49ef-80a8-b539bae529d3 · outbound

This paper cites Long short-term memory,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Long short-term memory,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T20:50:44.959528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7598c03d-b7be-49c4-857f-3cfc54c5cfc8 · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-04T20:50:44.963662Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 089d964f-00e2-4b39-b64c-90c29f521a34 · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T20:50:44.968179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa1afeb7-eb4a-4123-b6d9-8e07e8fdc306 · outbound

This paper cites Fast R-CNN,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Fast R-CNN,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.188354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4a383337-5bc1-49ea-ba1e-e191bac5afda · outbound

This paper cites Deep learning for precipitation nowcasting: a benchmark and a new model,.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Deep learning for precipitation nowcasting: a benchmark and a new model,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.172175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3fb25966-b95e-4421-b5aa-6cab89076ab5 · outbound

This paper cites Bidirectional LSTM-CRF Models for Sequence Tagging.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Bidirectional LSTM-CRF Models for Sequence Tagging

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T20:50:44.981883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51f1b4de-2fdf-4e6d-b103-1869b690a44e · outbound

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

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Informer: beyond efficient transformer for long sequence time-series forecasting,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-04T20:50:45.153756Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8cbb9c1b-bda2-4041-a0fe-1727c9111fbb · outbound

This paper cites Integrative Deep Learning Framework for Parkinson's Disease Early Detection using Gait Cycle Data Measured by Wearable Sensors: A CNN-GRU-GNN Approach.

GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python Integrative Deep Learning Framework for Parkinson's Disease Early Detection using Gait Cycle Data Measured by Wearable Sensors: A CNN-GRU-GNN Approach

Reference 26

Resolution: verified exact; local_arxiv, observed 2026-08-04T20:50:45.037660Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

No inbound Pith citation observations are available.