Paper Citation Record · LEDGER

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting

As of 20 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2507.19513.

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

pith.paper-citation-record.v1
2507.19513 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:27:02.563965Z

measured 43 of 43 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

43 of 43 outbound references displayed

verified exact3
verified fuzzy12
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch7

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5b2d5cd7-94a4-4a36-a945-f7342de1c16f · outbound

This paper cites Intelligent traffic adaptive resource allocation for edge computing-based 5g networks,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Intelligent traffic adaptive resource allocation for edge computing-based 5g networks,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:02.400425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef2eb62e-9273-49cd-9867-eb9a35ed7435 · outbound

This paper cites Predictive uav base station deployment and service offloading with distributed edge learning,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Predictive uav base station deployment and service offloading with distributed edge learning,

Reference 2

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:27:03.891891Z

Source-reported events for the cited work

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Observation aaf1cd8d-9893-4ecc-94f8-66efdea9cbe0 · outbound

This paper cites Millimeter-wave base station deployment using the scenario sampling approach,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Millimeter-wave base station deployment using the scenario sampling approach,

Reference 3

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:27:03.822327Z

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Observation 2cd3e6d5-d118-4fde-9149-dacb9fdc9aad · outbound

This paper cites Joint base station and irs deployment for enhancing network coverage: A graph-based modeling and optimization approach,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Joint base station and irs deployment for enhancing network coverage: A graph-based modeling and optimization approach,

Reference 4

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:27:03.744888Z

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Observation e4dd9e45-e2a9-42ff-9cd2-ee6a75773a6a · outbound

This paper cites an unresolved cited work.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-06T16:27:04.108677Z

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 ca04f255-b819-4c00-811f-068254881cfd · outbound

This paper cites Long short-term memory,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Long short-term memory,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:02.422104Z

Source-reported events for the cited work

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Observation 1b7ae050-c792-451e-a0ed-56ef5421b1b3 · outbound

This paper cites Lstm fully convolutional networks for time series classification,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Lstm fully convolutional networks for time series classification,

Reference 7

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:27:03.672809Z

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 92850d55-ee7a-4a6e-9011-6b0d918ad90d · outbound

This paper cites Multivariate lstm-fcns for time series classification,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Multivariate lstm-fcns for time series classification,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:27:04.098473Z

Source-reported events for the cited work

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Observation 7f3628d1-12f8-4c9b-9046-6f36436b98e8 · outbound

This paper cites ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:02.438217Z

Source-reported events for the cited work

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Observation 168f5380-e02d-4208-b54e-7dbe099e64a7 · outbound

This paper cites Minirocket: A very fast (almost) deterministic transform for time series classification,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Minirocket: A very fast (almost) deterministic transform for time series classification,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:02.441434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e8123ab-231a-4c6f-93d7-be737b4da031 · outbound

This paper cites Temporal aggregation of univariate and multivariate time series models: A survey,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Temporal aggregation of univariate and multivariate time series models: A survey,

Reference 11

Resolution: verified exact; raw_fallback, observed 2026-08-06T16:27:03.510712Z

Source-reported events for the cited work

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Observation 469e2083-c6da-466a-a5f7-8756fe3ed564 · outbound

This paper cites The great multivariate time series classification bake off: A review and experimental evaluation of recent algorithmic advances,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting The great multivariate time series classification bake off: A review and experimental evaluation of recent algorithmic advances,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:02.448637Z

Source-reported events for the cited work

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Observation 02573eb2-be34-4a97-945d-b40a015ba54a · outbound

This paper cites Transformers in time series: a survey,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Transformers in time series: a survey,

Reference 13

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Source-reported events for the cited work

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Observation 9629865c-a69c-4189-9c39-da9c51507fc0 · outbound

This paper cites Convolutional lstm network: a machine learning approach for precipitation nowcasting,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Convolutional lstm network: a machine learning approach for precipitation nowcasting,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:27:04.088627Z

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 7e7b69b4-dfba-438f-907d-2ef5007049d5 · outbound

This paper cites Long-term mobile traffic forecasting using deep spatio-temporal neural networks,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Long-term mobile traffic forecasting using deep spatio-temporal neural networks,

Reference 15

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:27:03.419718Z

Source-reported events for the cited work

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Observation e5ee839d-6400-4cea-b600-6310c52fd5e5 · outbound

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

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Connecting the dots: Multivariate time series forecasting with graph neural networks,

Reference 16

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Source-reported events for the cited work

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Observation e61db50e-b585-4c41-9df3-8a82c9a70ef9 · outbound

This paper cites Long-range transformers for dynamic spatiotemporal forecasting,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Long-range transformers for dynamic spatiotemporal forecasting,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:27:04.078016Z

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 98e68108-0d0f-4893-bbf5-53bec8e9b732 · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting xLSTM: Extended Long Short-Term Memory

Reference 18

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Source-reported events for the cited work

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Observation 2125a0f2-08ab-40c0-ab37-076e40faab07 · outbound

This paper cites Short-term traffic forecasting: Where we are and where we’re going,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Short-term traffic forecasting: Where we are and where we’re going,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:02.472556Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 155c8a82-a143-4549-988f-175c60830f47 · outbound

This paper cites Base station mobile traffic prediction based on arima and lstm model,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Base station mobile traffic prediction based on arima and lstm model,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:27:04.066740Z

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 9ef039d8-2d7d-494c-989d-169725bc3d1f · outbound

This paper cites A survey on deep learning for cellular traffic prediction,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting A survey on deep learning for cellular traffic prediction,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:02.479688Z

Source-reported events for the cited work

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Observation 11c116e4-7bda-4e88-a6be-41ca5f8f9f49 · outbound

This paper cites Deep spatio-temporal adaptive 3d convolutional neural networks for traffic flow prediction,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Deep spatio-temporal adaptive 3d convolutional neural networks for traffic flow prediction,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:27:04.056393Z

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 81a0a8dc-b600-48db-af29-ff45ca8413ff · outbound

This paper cites Graph wavenet for deep spatial-temporal graph modeling,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Graph wavenet for deep spatial-temporal graph modeling,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:27:04.044780Z

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 457001d5-b2ee-4a5a-b617-19b4e38baf07 · outbound

This paper cites STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting STGformer: Efficient Spatiotemporal Graph Transformer for Traffic Forecasting

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:02.494301Z

Source-reported events for the cited work

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Observation 47151731-0c07-43e7-9e34-3c0f220aad94 · outbound

This paper cites Citywide mobile traffic forecasting using spatial-temporal downsampling transformer neural networks,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Citywide mobile traffic forecasting using spatial-temporal downsampling transformer neural networks,

Reference 25

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Source-reported events for the cited work

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Observation 82ef8c1c-ab52-49e0-a557-ba0287af2977 · outbound

This paper cites Adaptive multi-receptive field spatial-temporal graph convolutional network for traffic forecasting,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Adaptive multi-receptive field spatial-temporal graph convolutional network for traffic forecasting,

Reference 26

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1a9c9efe-d825-4c35-a0b9-59f9a58ba368 · outbound

This paper cites Joint spatial and temporal classi- fication of mobile traffic demands,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Joint spatial and temporal classi- fication of mobile traffic demands,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:27:04.032029Z

Source-reported events for the cited work

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Observation 9407f73b-c902-4fc7-95ae-e322f50f4f91 · outbound

This paper cites Understanding mobile traffic patterns of large scale cellular towers in urban environment,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Understanding mobile traffic patterns of large scale cellular towers in urban environment,

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54773d83-1935-4a32-a41b-39107be37055 · outbound

This paper cites The prediction analysis of cellular radio access network traffic: From entropy theory to networking practice,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting The prediction analysis of cellular radio access network traffic: From entropy theory to networking practice,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:27:04.019715Z

Source-reported events for the cited work

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Observation 79e487d6-d704-45a1-b6b0-7612092328b2 · outbound

This paper cites Context- based interpretable spatio-temporal graph convolutional network for human motion forecasting,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Context- based interpretable spatio-temporal graph convolutional network for human motion forecasting,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:27:04.007132Z

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

Observation 942dc1ed-fbf0-4d0e-9cd0-1d4b7ed02053 · outbound

This paper cites Improving precipitation nowcasting using a three-dimensional convolutional neural network model from multi parameter phased array weather radar observations,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Improving precipitation nowcasting using a three-dimensional convolutional neural network model from multi parameter phased array weather radar observations,

Reference 31

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:27:02.984197Z

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

Observation d3a13b21-fe4f-4200-90bc-22c80b129f58 · outbound

This paper cites Graph dual-stream convolutional attention fusion for precipitation nowcasting,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Graph dual-stream convolutional attention fusion for precipitation nowcasting,

Reference 32

Resolution: metadata mismatch; raw_fallback, observed 2026-08-06T16:27:02.912861Z

Source-reported events for the cited work

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Observation ab2aaad8-0c01-4ef0-87c5-584381ce3069 · outbound

This paper cites Residual networks behave like ensembles of relatively shallow networks,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Residual networks behave like ensembles of relatively shallow networks,

Reference 33

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

Observation e8183328-af93-4382-a23a-0eaf91b27a82 · outbound

This paper cites Attention is all you need,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Attention is all you need,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:27:03.984398Z

Source-reported events for the cited work

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Observation 0f29f63c-50cb-4c72-a418-3f9916c8e8bc · outbound

This paper cites Self-attention with relative position representations,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Self-attention with relative position representations,

Reference 35

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Source-reported events for the cited work

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Observation 886fca97-7f69-4de2-863f-28ff17b68ecd · outbound

This paper cites Cross-modal attention for multi- modal image registration,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Cross-modal attention for multi- modal image registration,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:27:03.972367Z

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

Observation d8383bf1-09cf-48a1-a41e-dd97aafc5f78 · outbound

This paper cites Layer Normalization.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Layer Normalization

Reference 37

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Observation 93aaeff5-4a56-4773-b0e7-ec84c3eef610 · outbound

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Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Deep residual learning for image recognition,

Reference 38

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Observation d64d085a-d1c8-47d6-a27f-e0179d234ba4 · outbound

This paper cites Available: https://doi.org/10.1016/j.media.2022.102612.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Available: https://doi.org/10.1016/j.media.2022.102612

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:02.548263Z

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Observation 9bccd892-683b-47d2-a52d-9d31e6eef5b1 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Adam: A Method for Stochastic Optimization

Reference 40

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Source-reported events for the cited work

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Observation 7e1ad8bb-021c-4f14-b7b0-f1652246847d · outbound

This paper cites A multi-source dataset of urban life in the city of milan and the province of trentino,.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting A multi-source dataset of urban life in the city of milan and the province of trentino,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T16:27:02.559950Z

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Observation 18f6c400-8f59-407c-85fc-7e5dc18ec63a · outbound

This paper cites Available: https://doi.org/10.1016/j.neunet.2019.04.014.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Available: https://doi.org/10.1016/j.neunet.2019.04.014

Reference 2019

Resolution: verified exact; doi, observed 2026-08-06T16:27:02.703102Z

Source-reported events for the cited work

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Observation dac33185-6b79-4eea-9c7f-3e3c2923d111 · outbound

This paper cites Available: https://doi.org/10.1145/3510829.

Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting Available: https://doi.org/10.1145/3510829

Reference 2022

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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.