Paper Citation Record · LEDGER

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors

As of 19 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2411.11340.

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

pith.paper-citation-record.v1
2411.11340 v1

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T18:41:37.068184Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

21 of 21 outbound references displayed

verified exact2
verified fuzzy6
unresolved13
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f8892f5f-95dc-40a8-b225-9d0a411a9437 · outbound

This paper cites TSMixer: An All-MLP Architecture for Time Series Forecasting.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors TSMixer: An All-MLP Architecture for Time Series Forecasting

Reference 1

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Source-reported events for the cited work

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Observation c2af0247-930d-4ebc-9bdf-92b31125461d · outbound

This paper cites Therefore, we show the results of the forecasting part with the settings of the input length 96 and prediction length{192, 336, 720} here, respectively.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors Therefore, we show the results of the forecasting part with the settings of the input length 96 and prediction length{192, 336, 720} here, respectively

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:41:37.320964Z

Source-reported events for the cited work

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Observation 0badcceb-9e30-416c-b655-5c2dc3fb989f · outbound

This paper cites an unresolved cited work.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors Unresolved cited work

Reference 8

Resolution: verified exact; raw_fallback, observed 2026-08-12T18:41:37.208451Z

Source-reported events for the cited work

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Observation 6db70d80-75b7-40da-a4b0-65e1971c0628 · outbound

This paper cites For nation- illness dataset, the input length is 104 and prediction lengths are {24, 36, 48, 60 }, respectively.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors For nation- illness dataset, the input length is 104 and prediction lengths are {24, 36, 48, 60 }, respectively

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:41:37.348385Z

Source-reported events for the cited work

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Observation 17b3ab79-00a4-44f2-8843-bb20aaea3647 · outbound

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

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors Less Is More: Fast Multivariate Time Series Forecasting with Light Sampling-oriented MLP Structures

Reference 14

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Source-reported events for the cited work

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Observation ad29de28-c6cb-45b8-a186-c4ad740ba991 · outbound

This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:41:37.365497Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 923b14ba-d60a-49f3-881e-de16cc77a74b · outbound

This paper cites Datasets details.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors Datasets details

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:41:37.356661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 14d1457b-29be-44eb-b3a1-154be81d5357 · outbound

This paper cites an unresolved cited work.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors Unresolved cited work

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 85f12dab-300d-4720-9f39-42aa12e8c84b · outbound

This paper cites The results are presented in Table.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors The results are presented in Table

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:41:37.329444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a587abfb-696f-409a-bdf0-11dc49f1bb1b · outbound

This paper cites A decoder-only foundation model for time-series forecasting.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors A decoder-only foundation model for time-series forecasting

Reference 1990

Resolution: unresolved; no resolver link, observed 2026-08-12T18:41:37.009238Z

Source-reported events for the cited work

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Observation bf2f869b-9813-4119-887e-bdda84de4b21 · outbound

This paper cites Monash Time Series Forecasting Archive.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors Monash Time Series Forecasting Archive

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-12T18:41:37.016115Z

Source-reported events for the cited work

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Observation 71a9dff4-7160-4296-af2b-90f9d186d0dd · outbound

This paper cites ETSformer: Exponential Smoothing Transformers for Time-series Forecasting.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-12T18:41:37.041902Z

Source-reported events for the cited work

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Observation 3fec3f4a-ca2c-42f6-8d0f-ea7bc3c3990a · outbound

This paper cites Attention Is All You Need.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors Attention Is All You Need

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-12T18:41:37.035488Z

Source-reported events for the cited work

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Observation 4e397238-2889-441d-9e0c-758ab4c54872 · outbound

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

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-12T18:41:37.025583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f44f5195-b724-4226-9c41-ba0cb538aef3 · outbound

This paper cites TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-12T18:41:37.038832Z

Source-reported events for the cited work

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Observation 6dd3375f-2f93-4bb2-a3dc-9d407ab2e8e3 · outbound

This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-12T18:41:37.022090Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 78a52c64-6727-4f6b-a017-9f1a0283c71f · outbound

This paper cites Sliding empirical mode decomposition.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors Sliding empirical mode decomposition

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T18:41:37.373834Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 4045946f-363f-4728-8005-d8a07e6781f7 · outbound

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

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-12T18:41:37.044652Z

Source-reported events for the cited work

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Observation c6e437ed-1b80-482a-805d-66622e7f1124 · outbound

This paper cites GPT-4 Technical Report.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors GPT-4 Technical Report

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-12T18:41:37.029187Z

Source-reported events for the cited work

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Observation 77c06b4f-99a1-4eb8-9ffb-6f1fcc51f894 · outbound

This paper cites A review and discussion of decomposition-based hybrid models for wind energy forecasting applications.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors A review and discussion of decomposition-based hybrid models for wind energy forecasting applications

Reference 2023

Resolution: verified exact; doi, observed 2026-08-12T18:41:37.095071Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9a380bb1-aebe-4514-99d0-23206fea2c10 · outbound

This paper cites Time-LLM: Time Series Forecasting by Reprogramming Large Language Models.

A Hybrid Loss Framework for Decomposition-based Time Series Forecasting Methods: Balancing Global and Component Errors Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-12T18:41:37.019105Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.