Paper Citation Record · LEDGER

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis

As of 8 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2505.22474.

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

pith.paper-citation-record.v1
2505.22474 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:13:13.912772Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

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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

35 of 35 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation eb6a2710-7186-4c0b-8308-5df2af44256e · outbound

This paper cites Electricity load forecasting: a systematic review.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Electricity load forecasting: a systematic review

Reference 1

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This paper cites an unresolved cited work.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Unresolved cited work

Reference 2

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Observation 0f4210e4-794b-4d99-bfdc-4800a4d8cc9c · outbound

This paper cites Graphy: Graph-based physics-guided urban air quality modeling for monitoring-constrained regions.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Graphy: Graph-based physics-guided urban air quality modeling for monitoring-constrained regions

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:18.030400Z

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Observation b9157966-d766-4a27-ad91-8bbca2624ae2 · outbound

This paper cites Subseasonalclimateusa: a dataset for subseasonal forecasting and benchmarking.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Subseasonalclimateusa: a dataset for subseasonal forecasting and benchmarking

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:17.853157Z

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Observation 693b6b81-ba35-4232-812f-7fb66dd490f3 · outbound

This paper cites Carboncast: multi-day forecasting of grid carbon intensity.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Carboncast: multi-day forecasting of grid carbon intensity

Reference 5

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Observation abfecaa8-4026-4299-a243-9b1da8b46b46 · outbound

This paper cites Connecting the dots: Multivariate time series forecasting with graph neural networks.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Connecting the dots: Multivariate time series forecasting with graph neural networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:17.663565Z

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Observation b372348b-c20b-4465-b791-abdd12e010ea · outbound

This paper cites Graph Attention Networks.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Graph Attention Networks

Reference 7

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Observation 8f9eb747-89d7-4193-97cf-61e6ed672518 · outbound

This paper cites How Attentive are Graph Attention Networks?.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis How Attentive are Graph Attention Networks?

Reference 8

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

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 9

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Observation 1d263b5d-f943-4b60-a119-96d84b33f227 · outbound

This paper cites Carbon-aware computing for datacenters.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Carbon-aware computing for datacenters

Reference 10

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Observation 77d521ec-369f-42d4-8c3a-bef8e8f85d3a · outbound

This paper cites Some recent advances in forecasting and control.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Some recent advances in forecasting and control

Reference 11

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Observation ca4a023a-0f2a-408b-831c-9845a7a4aee6 · outbound

This paper cites Distribution of residual autocorrelations in autoregressive-integrated moving average time series models.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Distribution of residual autocorrelations in autoregressive-integrated moving average time series models

Reference 12

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Observation 9627d141-6b40-46e1-a28f-6d42622a9ced · outbound

This paper cites Forecasting seasonals and trends by exponentially weighted moving averages.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Forecasting seasonals and trends by exponentially weighted moving averages

Reference 13

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Observation de2e05b1-215a-47ac-b7b7-ff8104334b15 · outbound

This paper cites Forecasting sales by exponentially weighted moving averages.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Forecasting sales by exponentially weighted moving averages

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:16.898775Z

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Observation 9dc83119-518f-4973-ab64-9dee184b8ba5 · outbound

This paper cites A training algorithm for optimal margin classifiers.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis A training algorithm for optimal margin classifiers

Reference 15

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Observation ec1557c1-7c39-4fd0-818a-b4407ade0c68 · outbound

This paper cites Random forests.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Random forests

Reference 16

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Observation d6cb4820-9428-4966-bcaa-10ea6f54658e · outbound

This paper cites Greedy function approximation: a gradient boosting machine.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Greedy function approximation: a gradient boosting machine

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:16.460293Z

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Observation 457e3564-faa3-4714-8fd8-7e02edec3aeb · outbound

This paper cites Xgboost: A scalable tree boosting system.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Xgboost: A scalable tree boosting system

Reference 18

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Observation f222a648-3417-4290-a6ea-18b113d424e2 · outbound

This paper cites Long short-term memory.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Long short-term memory

Reference 19

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Observation 64851d8d-aa32-48aa-91ce-909b2b6d9d56 · outbound

This paper cites Empirical evaluation of gated recurrent neural networks on sequence modeling.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Empirical evaluation of gated recurrent neural networks on sequence modeling

Reference 20

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Observation a1d3edd9-e74a-46ff-b7ed-652df3a32301 · outbound

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

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis N-BEATS: Neural basis expansion analysis for interpretable time series forecasting

Reference 21

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Observation 67b3ee9d-34ca-461e-9900-760e34abea7f · outbound

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

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T13:13:11.985198Z

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Observation aaf6368e-e7e4-4be1-a29e-0378393c6b4a · outbound

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

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting

Reference 23

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Observation b02501ff-b38c-4a46-b9a0-e73d071b0dbc · outbound

This paper cites Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting

Reference 24

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Observation ca0570e2-f256-4241-991b-cb2b93889811 · outbound

This paper cites Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121--11128, 2023.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121--11128, 2023

Reference 25

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Observation 76d4b8c6-46ed-4471-b060-d37eb9495d67 · outbound

This paper cites Cyclenet: Enhancing time series forecasting through modeling periodic patterns.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Cyclenet: Enhancing time series forecasting through modeling periodic patterns

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:16.026605Z

Source-reported events for the cited work

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Observation d2efc936-bfb5-4754-b303-77218abcbaa3 · outbound

This paper cites Multivariate time series forecasting with dynamic graph neural odes.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Multivariate time series forecasting with dynamic graph neural odes

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:15.792442Z

Source-reported events for the cited work

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Observation 8b367068-3627-42f4-9d35-ce8efd3b160b · outbound

This paper cites Adaptive spatio-temporal graph convolutional neural network for remaining useful life estimation.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Adaptive spatio-temporal graph convolutional neural network for remaining useful life estimation

Reference 28

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Observation a5e3c02e-4214-45f8-909a-92e9f8ad3505 · outbound

This paper cites Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Spatial-Temporal Fusion Graph Neural Networks for Traffic Flow Forecasting

Reference 29

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Observation 9e6d82f3-4345-4a83-8ebf-5ad67527e20c · outbound

This paper cites Spatio-temporal predictive modeling techniques for different domains: a survey.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Spatio-temporal predictive modeling techniques for different domains: a survey

Reference 30

Resolution: verified exact; doi, observed 2026-08-07T13:13:14.561981Z

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Observation 2ce53e76-f609-4ef1-b977-778593db83e3 · outbound

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

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis A decoder-only foundation model for time-series forecasting

Reference 31

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This paper cites Unified training of universal time series forecasting transformers.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Unified training of universal time series forecasting transformers

Reference 32

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Observation 7ff0834c-5a9e-464b-b11e-45eceb550427 · outbound

This paper cites Decompose and conquer: Time series forecasting with multiseasonal trend decomposition using loess.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Decompose and conquer: Time series forecasting with multiseasonal trend decomposition using loess

Reference 33

Resolution: verified exact; doi, observed 2026-08-07T13:13:14.231719Z

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No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b55ccc88-ebae-4bcd-b07a-d2e678395f95 · outbound

This paper cites Dynamic time warping.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Dynamic time warping

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:15.310561Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8dc4a1a4-6491-4923-a9c5-292e19c141ed · outbound

This paper cites Assessing beijing's pm2.5 pollution: severity, weather impact, apec and winter heating.

Forecasting Multivariate Urban Data via Decomposition and Spatio-Temporal Graph Analysis Assessing beijing's pm2.5 pollution: severity, weather impact, apec and winter heating

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T13:13:15.127620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

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