Paper Citation Record · LEDGER

TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2403.09898.

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

pith.paper-citation-record.v1
2403.09898 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 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 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:54:28.936317Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-03T00:27:29.268640Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7940efc5-6275-4301-8393-7aad98bd115f · inbound

A Survey of Mamba cites this paper.

A Survey of Mamba TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-23T22:13:30.601154Z

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 ad745a85-2fc7-46ee-acc4-beab317fbb0f · inbound

Dataset-Driven Channel Masks in Transformers for Multivariate Time Series cites this paper.

Dataset-Driven Channel Masks in Transformers for Multivariate Time Series TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-23T18:43:19.305683Z

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 ec5c8200-b81b-4f92-8799-af1359f1641d · inbound

TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state cites this paper.

TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:28.936317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62386fc6-bd72-4908-98fc-466ba0a9d42d · inbound

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection cites this paper.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:28.488280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3753aa41-973d-4714-a96f-bb0cac08247c · inbound

S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG cites this paper.

S2M2ECG: Spatio-temporal bi-directional State Space Model Enabled Multi-branch Mamba for ECG TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T11:11:40.069544Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3189a5b6-653e-4213-9d52-540aafd00c63 · inbound

FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting cites this paper.

FAME: Forecastability-Aware Mixture of Experts for Heterogeneous Time Series Forecasting TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-07-03T00:27:29.270539Z

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.