Paper Citation Record · LEDGER

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting

As of 13 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 1 inbound Pith citation observation for arXiv:2412.00053.

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

pith.paper-citation-record.v1
2412.00053 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:56:20.489876Z

measured 13 of 13 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T09:31:55.829045Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T09:32:15.760067Z

Reference resolution

12 of 12 outbound references displayed

verified exact1
verified fuzzy2
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 44e15caf-b8ba-4040-bef6-e032b1c112aa · outbound

This paper cites Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T13:56:20.427655Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f640ce7d-092f-4918-86b5-c8784969d351 · outbound

This paper cites AutoTimes: Autoregressive Time Series Forecasters via Large Language Models.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting AutoTimes: Autoregressive Time Series Forecasters via Large Language Models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T13:56:20.434275Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41a7b319-f399-4e42-9a0e-3f8b3858e420 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting LLaMA: Open and Efficient Foundation Language Models

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T13:56:20.441743Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06d6f2cf-dc91-4bc6-b09a-b5b22342f3c3 · outbound

This paper cites In contrast, the Traffic dataset shows consistent improvements up to three experts.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting In contrast, the Traffic dataset shows consistent improvements up to three experts

Reference 9

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-12T13:56:20.840932Z

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 f049a12d-5b69-4ee3-b486-5d6f345ba240 · outbound

This paper cites Language Models are Few-Shot Learners.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting Language Models are Few-Shot Learners

Reference 1970

Resolution: unresolved; no resolver link, observed 2026-08-12T13:56:20.414790Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e54bb612-86f1-45fa-bccd-81ddddbcee6b · outbound

This paper cites For example, to reduce the time complexity and memory usage, Informer (Wen et al.,.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting For example, to reduce the time complexity and memory usage, Informer (Wen et al.,

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:56:21.030888Z

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 9f51d50b-a56c-43b6-9bde-0c7ef5fe91d9 · outbound

This paper cites an unresolved cited work.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting Unresolved cited work

Reference 2016

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:56:21.052618Z

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 4c701dca-a41f-4e30-8354-8094ee6833a8 · outbound

This paper cites an unresolved cited work.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting Unresolved cited work

Reference 2018

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:56:21.073998Z

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 74a1ee93-22b1-41df-a59c-1524bd72b0c3 · outbound

This paper cites Both subsets span the period from July 2016 to July.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting Both subsets span the period from July 2016 to July

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:56:21.095414Z

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 714c1b82-b837-4d9a-9c2f-7daf3c3c0875 · outbound

This paper cites an unresolved cited work.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:56:21.007671Z

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 a1d5a74d-8ee9-4ead-ae92-202f3526bfa5 · outbound

This paper cites Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-12T13:56:20.447725Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48b55c78-aae2-4dd4-9d71-3594c086cb0c · outbound

This paper cites LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters.

LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting LLM4TS: Aligning Pre-Trained LLMs as Data-Efficient Time-Series Forecasters

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T13:56:20.420892Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation eaa946b1-ddeb-40d0-9814-c14c17d64b71 · inbound

From Time Series Analysis to Question Answering: A Survey in the LLM Era cites this paper.

From Time Series Analysis to Question Answering: A Survey in the LLM Era LeMoLE: LLM-Enhanced Mixture of Linear Experts for Time Series Forecasting

Reference 123

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:32:15.763273Z

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.