Paper Citation Record · LEDGER

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression

As of 20 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:1907.10865.

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

pith.paper-citation-record.v1
1907.10865 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-24T16:25:27.306124Z

measured 12 of 12 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

12 of 12 outbound references displayed

verified exact0
verified fuzzy11
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9626b32a-f0f5-488c-9ca0-81aef2874aaa · outbound

This paper cites an unresolved cited work.

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-05-24T16:26:16.409747Z

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 690b3708-035b-4076-af67-31163850d50d · outbound

This paper cites Spa tiotemporal mobility prediction in proactive self-organizing cellular networks.

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression Spa tiotemporal mobility prediction in proactive self-organizing cellular networks

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:26:16.425175Z

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 6a155f44-d8ae-48e2-aff0-a5aaa081edbf · outbound

This paper cites Intelligent 5G: When Cellular Networks Meet Artificial Intelligence.

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression Intelligent 5G: When Cellular Networks Meet Artificial Intelligence

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:26:16.402692Z

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 48f9c70e-26e2-43f6-92aa-cfa050c93644 · outbound

This paper cites Traffic Modeling and Prediction using ARIMA/GARCH Model.

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression Traffic Modeling and Prediction using ARIMA/GARCH Model

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:26:16.407478Z

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 6d9ede07-2e4a-483c-83e1-4807023f4fc1 · outbound

This paper cites The Learning and Prediction of Application-Level Traffic Data in Cellular Networks.

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression The Learning and Prediction of Application-Level Traffic Data in Cellular Networks

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:26:16.390779Z

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 e3287209-9bef-4fb7-955d-069fa0e48fb2 · outbound

This paper cites Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach.

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression Spatiotemporal modeling and prediction in cellular networks: A big data enabled deep learning approach

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:26:16.382099Z

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 295455ef-100e-49cc-8f25-e506319a6713 · outbound

This paper cites Network traffic prediction based on deep belief network in wireless mesh backbone networks.

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression Network traffic prediction based on deep belief network in wireless mesh backbone networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:26:16.411462Z

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 13ee1aef-b19c-40e3-86d7-476972287f18 · outbound

This paper cites Citywide Cellular Traffic Prediction Based on Densely Connected Convolutional Neural Networks.

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression Citywide Cellular Traffic Prediction Based on Densely Connected Convolutional Neural Networks

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:26:16.419186Z

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 6a39d9eb-a74d-4df5-a21a-0dd9e41d1585 · outbound

This paper cites Densely connected convolutional networks.

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression Densely connected convolutional networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:26:16.402982Z

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 864d5312-0293-4609-bb82-8f4d01be9b7b · outbound

This paper cites A multi-source dataset of urban life in the city of Milan and the Province of Trentino.

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression A multi-source dataset of urban life in the city of Milan and the Province of Trentino

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:26:16.413473Z

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 d61a0dfd-837f-4877-8d4f-77c16fdf993b · outbound

This paper cites Deep learning.

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression Deep learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:26:16.394072Z

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 f2e0ab87-a9c2-4d74-8fb9-57a46e02e3d6 · outbound

This paper cites Pixel-wise regression using U-Net and its application on pansharpening.

Forecasting Mobile Traffic with Spatiotemporal correlation using Deep Regression Pixel-wise regression using U-Net and its application on pansharpening

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-24T16:26:16.390268Z

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.