Paper Citation Record · LEDGER

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles

As of 14 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2506.13828.

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

pith.paper-citation-record.v1
2506.13828 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T00:42:04.649841Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

15 of 15 outbound references displayed

verified exact0
verified fuzzy11
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bdb88c5e-0e61-41ef-b693-438149ab3737 · outbound

This paper cites Da-rnn-based bus arrival time prediction model,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles Da-rnn-based bus arrival time prediction model,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:05.483238Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation bb259699-f1f8-47f9-b22d-7c8d8be7ef32 · outbound

This paper cites Attention-based deep recurrent neural network to forecast the temperature behavior of an electric arc furnace side-wall,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles Attention-based deep recurrent neural network to forecast the temperature behavior of an electric arc furnace side-wall,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:05.474433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 90cff720-a1a8-4999-93bc-306e20900be9 · outbound

This paper cites Water quality prediction based on recurrent neural network and improved evidence theory: A case study of qiantang river, china,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles Water quality prediction based on recurrent neural network and improved evidence theory: A case study of qiantang river, china,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:05.464889Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dff8f283-317d-4244-864f-bd0c78cfe2c1 · outbound

This paper cites A novel model based on da-rnn and skip-gru for periodic time series forecasting,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles A novel model based on da-rnn and skip-gru for periodic time series forecasting,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:05.455304Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 449c3f10-6830-4713-8d58-8c8a3278ded8 · outbound

This paper cites Detection of anomalies in data streams using the lstm-cnn model,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles Detection of anomalies in data streams using the lstm-cnn model,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:05.446485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 14b3fedc-ef1c-4b25-89d0-5620e43d0b55 · outbound

This paper cites A dual-stage attention-based recurrent neural network for time series prediction,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles A dual-stage attention-based recurrent neural network for time series prediction,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:05.437743Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 447c01d7-e286-4563-81f3-83f5acf53cf8 · outbound

This paper cites A hybrid cnn-lstm approach for anomaly detection in software-defined networks,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles A hybrid cnn-lstm approach for anomaly detection in software-defined networks,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:05.428686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0dc7f3f0-1eb7-402b-a456-15c8e815aed2 · outbound

This paper cites Da-lstm-vae: Dual-stage attention-based lstm-vae for kpi anomaly detection,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles Da-lstm-vae: Dual-stage attention-based lstm-vae for kpi anomaly detection,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:05.362159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 49f132f8-3e1b-4d58-959b-bd4a085c132f · outbound

This paper cites Anomaly Detection Based on Isolation Mechanisms: A Survey.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles Anomaly Detection Based on Isolation Mechanisms: A Survey

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:04.032648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f95a165b-f8d7-48cd-a095-5ff2b4ff0461 · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles Discovering governing equations from data by sparse identification of nonlinear dynamical systems,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:04.113990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8120eedf-f577-4fea-b73a-05661284ef22 · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:04.243887Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4f9ad00-450d-43a1-8d16-44e557053707 · outbound

This paper cites Energy load forecasting using a dual-stage attention-based recurrent neural network,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles Energy load forecasting using a dual-stage attention-based recurrent neural network,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:05.216690Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation df8ad539-1183-4f3d-a2bf-873911afd980 · outbound

This paper cites Generic and scalable framework for automated time-series anomaly detection,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles Generic and scalable framework for automated time-series anomaly detection,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:05.051508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation f1ba4e36-f2d3-467e-9237-4a7b1cea5f2f · outbound

This paper cites LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T00:42:04.536756Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 82d09f1e-ad1d-4927-a571-58a1b7c038c5 · outbound

This paper cites Da-lstm: A dynamic drift-adaptive learning framework for interval load forecasting with lstm networks,.

Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles Da-lstm: A dynamic drift-adaptive learning framework for interval load forecasting with lstm networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:42:04.810838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

No inbound Pith citation observations are available.