Paper Citation Record · LEDGER

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting

As of 15 August 2026, this Paper Citation Record lists 27 of 27 outbound references and 0 inbound Pith citation observations for arXiv:2412.06835.

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

pith.paper-citation-record.v1
2412.06835 v1

Coverage vector

measured 27 of 27 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:41:23.072379Z

measured 27 of 27 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

27 of 27 outbound references displayed

verified exact1
verified fuzzy14
unresolved12
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 02805c17-4b90-4306-bd76-7d9b69feb1ed · outbound

This paper cites Analysis of flash flood disaster characteristics in china from 2011 to 2015,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Analysis of flash flood disaster characteristics in china from 2011 to 2015,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.625561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ef5d3376-ac80-423f-a409-34e975312868 · outbound

This paper cites Long short-term memory,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Long short-term memory,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.604918Z

Source-reported events for the cited work

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Observation fd1337dd-0d3d-4fc0-ba6c-ff986435a3bd · outbound

This paper cites Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting

Reference 3

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Observation 171e379f-484b-40c1-9a92-3d571c9c70eb · outbound

This paper cites N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Reference 4

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Source-reported events for the cited work

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Observation 86c35922-dffa-4b2b-a667-0eb0c9fad7de · outbound

This paper cites Daily long-term traffic flow forecasting based on a deep neural network,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Daily long-term traffic flow forecasting based on a deep neural network,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.585645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 58b28ac3-7613-4f19-9b99-71f7d5d272c8 · outbound

This paper cites Periodic-crn: A con- volutional recurrent model for crowd density prediction with recurring periodic patterns.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Periodic-crn: A con- volutional recurrent model for crowd density prediction with recurring periodic patterns

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.565225Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 91116c9f-2620-4e6e-a3ff-9885dd4d7420 · outbound

This paper cites Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Modeling and forecasting vehicular traffic flow as a seasonal arima process: Theoretical basis and empirical results,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.545639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 03ed0ef8-2d38-4993-9c1e-e49a1e221ed1 · outbound

This paper cites Are transformers effective for time series forecasting?.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Are transformers effective for time series forecasting?

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.525522Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation d3130f20-31c3-48d4-87a4-6eccc70d655f · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Semi-Supervised Classification with Graph Convolutional Networks

Reference 9

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Source-reported events for the cited work

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Observation 858ee7fb-934b-4d91-abd8-d9d3488e911a · outbound

This paper cites Spatial-temporal graph ode networks for traffic flow forecasting,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Spatial-temporal graph ode networks for traffic flow forecasting,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.508783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 78ee1724-7687-438f-8fbe-ba4ce4063f5b · outbound

This paper cites Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Spatial-temporal synchronous graph convolutional networks: A new framework for spatial-temporal network data forecasting,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.492157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation c42cdbf4-9117-47e1-8d85-fb6c20141f23 · outbound

This paper cites Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Spatial-temporal identity: A simple yet effective baseline for multivariate time series forecasting,

Reference 12

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Source-reported events for the cited work

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Observation 0ec2c966-ef83-4a37-a23c-4aad9c50158f · outbound

This paper cites ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting ST-MLP: A Cascaded Spatio-Temporal Linear Framework with Channel-Independence Strategy for Traffic Forecasting

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:22.971492Z

Source-reported events for the cited work

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Observation 4e06b761-c1d7-45f0-9f4d-bee9f4bf3e40 · outbound

This paper cites Attention based spatial-temporal graph convolutional networks for traffic flow fore- casting,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Attention based spatial-temporal graph convolutional networks for traffic flow fore- casting,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.464520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation fdd3f99b-8ca4-42b0-bb44-34b688ac7126 · outbound

This paper cites Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Learning dynamics and heterogeneity of spatial-temporal graph data for traffic forecasting,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.446779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 2ecff4b3-ac14-44aa-8b09-dceaafa58a63 · outbound

This paper cites Timesnet: Temporal 2d-variation modeling for general time series analysis,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Timesnet: Temporal 2d-variation modeling for general time series analysis,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:22.993661Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4c93ed46-bf57-4bf8-b0b4-2921b8face27 · outbound

This paper cites A new flood forecasting model based on svm and boosting learning algorithms,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting A new flood forecasting model based on svm and boosting learning algorithms,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.410538Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation a184a52f-3638-45d0-bb3f-4eec8a39dccd · outbound

This paper cites Interpretable spatio- temporal attention lstm model for flood forecasting,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Interpretable spatio- temporal attention lstm model for flood forecasting,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.389533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b1dc144c-4e32-48a3-8097-0bcf2b05bf33 · outbound

This paper cites Graph convolution based spatial-temporal attention lstm model for flood forecasting,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Graph convolution based spatial-temporal attention lstm model for flood forecasting,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.367448Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation b1b06df0-aab1-4714-8471-2400b3daa776 · outbound

This paper cites Graph WaveNet for Deep Spatial-Temporal Graph Modeling.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Graph WaveNet for Deep Spatial-Temporal Graph Modeling

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:23.030215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9dab1224-256a-48f8-9c2d-1f72826a2e48 · outbound

This paper cites Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:41:23.343734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 69d91298-8bb6-4386-bb4e-bceb302da6f9 · outbound

This paper cites Deep residual learning for image recognition,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Deep residual learning for image recognition,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:23.042247Z

Source-reported events for the cited work

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Observation 14b2ef27-7254-489b-b38c-27de0556d939 · outbound

This paper cites Laplacian eigenmaps for dimensionality reduction and data representation,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Laplacian eigenmaps for dimensionality reduction and data representation,

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:23.047306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cdfa1315-3ec6-4233-a05d-5159ba27f8bc · outbound

This paper cites PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:41:23.177337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation cbff53f5-57ac-4fa4-9dbf-a1c4f0a9534d · outbound

This paper cites Neural Machine Translation by Jointly Learning to Align and Translate.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Neural Machine Translation by Jointly Learning to Align and Translate

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:23.057873Z

Source-reported events for the cited work

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Observation 92d2139e-1e31-4a20-bf4b-1fdfd1fc846d · outbound

This paper cites Attention is all you need,.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Attention is all you need,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:23.064751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b24bf18-890c-41c7-8094-2b7c2a5e9193 · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

APS-LSTM: Exploiting Multi-Periodicity and Diverse Spatial Dependencies for Flood Forecasting Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T20:41:23.072379Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

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