Paper Citation Record · LEDGER

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling

As of 10 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2505.16573.

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

pith.paper-citation-record.v1
2505.16573 v1

Coverage vector

measured 34 of 34 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-07T15:03:24.942226Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

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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

34 of 34 outbound references displayed

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External citation measurements

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Outbound references

Observation cdcf01ea-bd13-4858-b5c2-fbb96de2497c · outbound

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

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Distribution of residual autocorrelations in autoregressive-integrated moving average time series models,

Reference 1

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Observation be69f472-9dbe-4e2f-9316-fcf90b55bfc2 · outbound

This paper cites Forecasting time series using a methodology based on autoregressive integrated moving average and genetic program- ming,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Forecasting time series using a methodology based on autoregressive integrated moving average and genetic program- ming,

Reference 2

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Observation ac6e92bb-6b62-47bd-a659-cc8cf0a8f7e1 · outbound

This paper cites Comparison of arima and exponential smoothing models in prediction of stock prices,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Comparison of arima and exponential smoothing models in prediction of stock prices,

Reference 3

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Observation b49b81e3-9005-47b3-b655-cf5f398702c8 · outbound

This paper cites Exponential smoothing: The state of the art,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Exponential smoothing: The state of the art,

Reference 4

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Observation 0176a9ec-b168-4902-9379-7e45c46c6681 · outbound

This paper cites Exponential smoothing: The state of the art—part ii,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Exponential smoothing: The state of the art—part ii,

Reference 5

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Observation c3ba79db-1959-4033-9b5b-7e67744c5344 · outbound

This paper cites Predicting the brazilian stock market through neural networks and adaptive exponential smoothing methods,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Predicting the brazilian stock market through neural networks and adaptive exponential smoothing methods,

Reference 6

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Observation 7d4f7214-81c6-423d-b25b-2f18e8cca678 · outbound

This paper cites Sup- port vector machines,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Sup- port vector machines,

Reference 7

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Observation 5add61f7-9fab-4625-88ba-9dde203ef57c · outbound

This paper cites Decision trees,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Decision trees,

Reference 8

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Observation 1d72398c-341d-48df-8098-83327d3da298 · outbound

This paper cites Random forests,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Random forests,

Reference 9

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Observation c7ae9414-f5c5-4fc3-95ea-7817eca4b0a0 · outbound

This paper cites Finding structure in time,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Finding structure in time,

Reference 10

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Observation 419a429a-32bd-4292-8dd7-97b9bbdf6825 · outbound

This paper cites Long short-term memory,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Long short-term memory,

Reference 11

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Observation 4f4b64b8-0c2d-460c-813e-db1de700490e · outbound

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

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 12

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Observation a28a2e96-492b-4c8e-a1d3-fb22a801a625 · outbound

This paper cites Attention is all you need,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Attention is all you need,

Reference 13

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Observation e6e6ad8f-bc76-43bb-9628-bb64eb89faf5 · outbound

This paper cites FilterNet: Harnessing Frequency Filters for Time Series Forecasting.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling FilterNet: Harnessing Frequency Filters for Time Series Forecasting

Reference 14

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Observation b7d12dcb-811d-4ecf-9eaa-f4843122c0cc · outbound

This paper cites Frequency-domain mlps are more effective learners in time series forecasting,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Frequency-domain mlps are more effective learners in time series forecasting,

Reference 15

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Observation 1028c4ef-a0c3-439d-9a8f-200be2f2272c · outbound

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

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Are transformers effective for time series forecasting?

Reference 16

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Observation c282b547-45ef-4611-aaab-6260061ac8c8 · outbound

This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 17

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Observation 8a299d1e-daae-48d9-97b6-4da413b68635 · outbound

This paper cites A Time Series is Worth 64 Words: Long-term Forecasting with Transformers.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 18

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Observation cfd7058e-33ed-4440-a167-172b7ee0cbcb · outbound

This paper cites Cross-stock momentum and factor momentum,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Cross-stock momentum and factor momentum,

Reference 19

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Observation e9aa54cd-3627-401e-838c-2d5135732acc · outbound

This paper cites Diffusion variational autoencoder for tackling stochasticity in multi-step regression stock price prediction,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Diffusion variational autoencoder for tackling stochasticity in multi-step regression stock price prediction,

Reference 20

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Observation 004c7c87-7611-4c72-921e-c978c4a2e754 · outbound

This paper cites A Stock Price Prediction Approach Based on Time Series Decomposition and Multi-Scale CNN using OHLCT Images.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling A Stock Price Prediction Approach Based on Time Series Decomposition and Multi-Scale CNN using OHLCT Images

Reference 21

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Observation 78920028-5ea4-462c-ae9b-dc29b2398f92 · outbound

This paper cites A stock prediction method based on multidi- mensional and multilevel feature dynamic fusion,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling A stock prediction method based on multidi- mensional and multilevel feature dynamic fusion,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:28.218134Z

Source-reported events for the cited work

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Observation 8da56f5c-e15c-49c3-97be-51f76caa8f63 · outbound

This paper cites Multi-factor stock price prediction based on gan-trellisnet,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Multi-factor stock price prediction based on gan-trellisnet,

Reference 23

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Observation 2a12b388-f777-4a93-a7e3-891a54ef9160 · outbound

This paper cites Generative adversarial networks,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Generative adversarial networks,

Reference 24

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Observation 102a40eb-0208-464f-9269-c116ea8c1fd3 · outbound

This paper cites Trellis Networks for Sequence Modeling.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Trellis Networks for Sequence Modeling

Reference 25

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Observation 241bdeec-3215-4c89-942d-969e4661ad43 · outbound

This paper cites Fnspid: A comprehensive financial news dataset in time series,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Fnspid: A comprehensive financial news dataset in time series,

Reference 26

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Observation 36110426-13c6-4b76-9a44-5e91f94d3401 · outbound

This paper cites Effective exploitation of macroeco- nomic indicators for stock direction classification using the multimodal fusion transformer,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Effective exploitation of macroeco- nomic indicators for stock direction classification using the multimodal fusion transformer,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:27.532248Z

Source-reported events for the cited work

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Observation 1441ff71-def3-4c48-a8f9-4ee2a8e8ea93 · outbound

This paper cites The evolution of studies on social media sentiment in the stock market: Insights from bibliometric analy- sis,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling The evolution of studies on social media sentiment in the stock market: Insights from bibliometric analy- sis,

Reference 28

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Observation 767573c6-4726-4a50-8555-8c14bf5edb68 · outbound

This paper cites Survey on privacy-preserving machine learning,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Survey on privacy-preserving machine learning,

Reference 29

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Observation 17d419fe-e2ff-4bf5-a4be-32882b8be74f · outbound

This paper cites Privacy-preserving deep learning on machine learning as a service—a comprehensive survey,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Privacy-preserving deep learning on machine learning as a service—a comprehensive survey,

Reference 30

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This paper cites {SWIFT}: Super-fast and robust privacy-preserving machine learning,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling {SWIFT}: Super-fast and robust privacy-preserving machine learning,

Reference 31

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Observation 3373113b-25dd-4f4a-9dfb-8394f107c9b4 · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Federated Learning: Strategies for Improving Communication Efficiency

Reference 32

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This paper cites Communication-efficient learning of deep networks from decentralized data,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Communication-efficient learning of deep networks from decentralized data,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:03:26.104284Z

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This paper cites Pytorch: An impera- tive style, high-performance deep learning library,.

From Local Patterns to Global Understanding: Cross-Stock Trend Integration for Enhanced Predictive Modeling Pytorch: An impera- tive style, high-performance deep learning library,

Reference 34

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Source-reported events for the cited work

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Pith citing papers

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