Paper Citation Record · LEDGER

Gradient Boosting Decision Tree with LSTM for Investment Prediction

As of 7 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2505.23084.

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

pith.paper-citation-record.v1
2505.23084 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:57:25.688366Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

25 of 25 outbound references displayed

verified exact0
verified fuzzy22
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bf83853d-1469-44e5-a496-81812caa4871 · outbound

This paper cites Xgboost: A scalable tree boosting system.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Xgboost: A scalable tree boosting system

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T12:57:23.368312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 806b7631-049c-425a-a290-93c15ce08b11 · outbound

This paper cites Schubert, and Liang Dong.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Schubert, and Liang Dong

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:30.214538Z

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 5f92227c-233d-4dcd-aae2-4d7818a3b6ca · outbound

This paper cites Ai-driven prognostics for state of health prediction in li-ion batteries: A comprehensive analysis with validation, 2025.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Ai-driven prognostics for state of health prediction in li-ion batteries: A comprehensive analysis with validation, 2025

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:30.030201Z

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 c8031e9c-aa2a-416c-91ec-a10ac2d6e262 · outbound

This paper cites Nerf-based defect detection, 2025.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Nerf-based defect detection, 2025

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:29.868236Z

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 ad91930e-b513-4e2a-b350-07f98fbe2627 · outbound

This paper cites Embracing the informative missingness and silent gene in analyzing biologically diverse samples.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Embracing the informative missingness and silent gene in analyzing biologically diverse samples

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:29.643005Z

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 54664e14-e296-4ae6-a7b9-617c59ebb5de · outbound

This paper cites Power system transient stability assessment based on snapshot ensemble lstm network.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Power system transient stability assessment based on snapshot ensemble lstm network

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:29.432962Z

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 322d4112-1c65-45b7-af3b-0bafda0dbc43 · outbound

This paper cites an unresolved cited work.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-07T12:57:29.220610Z

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 d5e78a70-ccbc-4bef-ac89-72dd235350f7 · outbound

This paper cites A survey of machine learning algorithms for defective steel plates classification.

Gradient Boosting Decision Tree with LSTM for Investment Prediction A survey of machine learning algorithms for defective steel plates classification

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:29.024827Z

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 ac25d540-c50d-4e55-8e2c-f8023d61fbb2 · outbound

This paper cites HermEs: Interactive spreadsheet formula prediction via hierarchical formulet expansion.

Gradient Boosting Decision Tree with LSTM for Investment Prediction HermEs: Interactive spreadsheet formula prediction via hierarchical formulet expansion

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:28.829182Z

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 b218ee48-e305-458d-b3fb-1dde22c9e973 · outbound

This paper cites Long short-term memory.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Long short-term memory

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:28.612174Z

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 0188fb49-da61-4c4e-bc9d-00e8acb2631b · outbound

This paper cites Learning from teaching regularization: Generalizable correlations should be easy to imitate.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Learning from teaching regularization: Generalizable correlations should be easy to imitate

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:28.441753Z

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 19aafb6d-986c-41c4-bb93-67dbfc2cd745 · outbound

This paper cites Kkbox music recommendation challenge.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Kkbox music recommendation challenge

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:28.215421Z

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 d2b38a70-dc54-41df-ae25-c0297bc6aa04 · outbound

This paper cites 6: Simultaneous tracking, tagging and mapping for augmented reality.

Gradient Boosting Decision Tree with LSTM for Investment Prediction 6: Simultaneous tracking, tagging and mapping for augmented reality

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:27.971070Z

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 85392035-a906-4a20-be25-6b40d2dd96a1 · outbound

This paper cites Lightgbm: A highly efficient gradient boosting decision tree.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Lightgbm: A highly efficient gradient boosting decision tree

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T12:57:24.702377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5b241eb-e85a-4bed-96fc-65f99e2d8562 · outbound

This paper cites Memory mechanism for unsupervised anomaly detection.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Memory mechanism for unsupervised anomaly detection

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:27.733126Z

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 91e46fda-a381-4237-b774-0e09caa0e3e4 · outbound

This paper cites Hyman: Hybrid memory and attention network for unsupervised anomaly detection.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Hyman: Hybrid memory and attention network for unsupervised anomaly detection

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:27.519070Z

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 3f0c0194-5283-4803-b2e6-b17afb61c3ff · outbound

This paper cites Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Research on reinforcement learning based warehouse robot navigation algorithm in complex warehouse layout

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:27.323185Z

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 4cd21df0-a9fe-4ab1-b1ef-31390bfe3702 · outbound

This paper cites Cot: an efficient and accurate method for detecting marker genes among many subtypes.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Cot: an efficient and accurate method for detecting marker genes among many subtypes

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:27.104131Z

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 1903e7d3-7303-437e-92b1-c8ebe850cf31 · outbound

This paper cites A communication-efficient parallel algorithm for decision tree.

Gradient Boosting Decision Tree with LSTM for Investment Prediction A communication-efficient parallel algorithm for decision tree

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:26.947530Z

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 a405f327-b861-45c2-841c-8cf55219dd98 · outbound

This paper cites Catboost: unbiased boosting with categorical features.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Catboost: unbiased boosting with categorical features

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:26.773722Z

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 2cbde769-6fe3-412c-acfd-43e15e4be738 · outbound

This paper cites GPT-signal: Generative AI for semi-automated feature engineering in the alpha research process.

Gradient Boosting Decision Tree with LSTM for Investment Prediction GPT-signal: Generative AI for semi-automated feature engineering in the alpha research process

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:26.589691Z

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 5efc9d9d-0d34-462f-929e-56b477161684 · outbound

This paper cites Stacked generalization.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Stacked generalization

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:26.425809Z

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 09269d91-dd8a-494e-8e4f-5587f70ed781 · outbound

This paper cites Measuring digitalization capabilities using machine learning.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Measuring digitalization capabilities using machine learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:26.253689Z

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 5b942c2d-1590-46b4-945d-3553db6440d6 · outbound

This paper cites Machine learning optimizes the efficiency of picking and packing in automated warehouse robot systems.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Machine learning optimizes the efficiency of picking and packing in automated warehouse robot systems

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:26.072664Z

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 48edccf8-a04e-42be-a6c3-148e3f0635f2 · outbound

This paper cites Rhyme-aware chinese lyric generator based on gpt.

Gradient Boosting Decision Tree with LSTM for Investment Prediction Rhyme-aware chinese lyric generator based on gpt

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:57:25.898023Z

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

No inbound Pith citation observations are available.