Paper Citation Record · LEDGER

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting

As of 11 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2412.12883.

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

pith.paper-citation-record.v1
2412.12883 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T13:41:21.153916Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:05:54.981663Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

51 of 51 outbound references displayed

verified exact5
verified fuzzy1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 217c7135-6b50-480c-bca2-91d6c1fad9da · outbound

This paper cites an unresolved cited work.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

Reference 1

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Observation 3902e236-7514-40fc-a22a-048701a04a97 · outbound

This paper cites Token Merging: Your ViT But Faster.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Token Merging: Your ViT But Faster

Reference 2

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Observation 597733b3-2569-4cb0-bf99-934585b4147a · outbound

This paper cites Comprehensive Survey of Model Compression and Speed up for Vision Transformers.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Comprehensive Survey of Model Compression and Speed up for Vision Transformers

Reference 3

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Observation c4b731ff-205a-44b2-822d-5b9089ac91ab · outbound

This paper cites Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Personalized Adapter for Large Meteorology Model on Devices: Towards Weather Foundation Models

Reference 4

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Observation 5be4b879-acd9-46c1-b073-d86f78a6a092 · outbound

This paper cites A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations

Reference 5

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This paper cites A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations

Reference 6

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This paper cites an unresolved cited work.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

Reference 7

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

Reference 8

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This paper cites Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Preformer: Predictive Transformer with Multi-Scale Segment-wise Correlations for Long-Term Time Series Forecasting

Reference 9

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

Reference 10

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

Reference 11

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Observation 54f1e24a-035f-47ff-9584-d9b5b9dff5c8 · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 12

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This paper cites A Systematic Literature Survey of Sparse Matrix-Vector Multiplication.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting A Systematic Literature Survey of Sparse Matrix-Vector Multiplication

Reference 13

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

Reference 14

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Observation 3688a21e-d301-4bdb-939d-2e43c938c6b2 · outbound

This paper cites Hagiwara.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Hagiwara

Reference 15

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Observation b79c0e34-e562-4190-bbf2-bc603747abcd · outbound

This paper cites Deep Residual Learning for Image Recognition.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Deep Residual Learning for Image Recognition

Reference 16

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

Reference 17

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This paper cites Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

Reference 18

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Observation 5d4c07d7-3729-4a76-b6f0-3b1aa2db6f74 · outbound

This paper cites LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting LLM-based Knowledge Pruning for Time Series Data Analytics on Edge-computing Devices

Reference 19

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This paper cites Scaling Laws for Neural Language Models.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Scaling Laws for Neural Language Models

Reference 20

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Scaling Laws for Neural Language Models

Reference 21

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Adam: A Method for Stochastic Optimization

Reference 22

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

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This paper cites Liu, and Schahram Dustdar.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Liu, and Schahram Dustdar

Reference 26

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

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This paper cites Scaling transformer neural networks for skillful and reliable medium-range weather forecasting.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Scaling transformer neural networks for skillful and reliable medium-range weather forecasting

Reference 29

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training

Reference 30

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 36

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting A Survey on Transformer Compression

Reference 37

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Attention Is All You Need

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Recent Advances on Neural Network Pruning at Initialization

Reference 40

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Enhancing Transformer Efficiency for Multivariate Time Series Classification

Reference 41

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Transformers in Time Series: A Survey

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Transformers in Time Series: A Survey

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Weyrauch, T

Reference 44

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting

Reference 45

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Unresolved cited work

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

Reference 48

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Reference 49

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting

Reference 51

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A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting A Survey of Lottery Ticket Hypothesis

Reference 2024

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Pith citing papers

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AQUA: Attention via QUery mAgnitudes for Memory and Compute Efficient Inference in LLMs cites this paper.

AQUA: Attention via QUery mAgnitudes for Memory and Compute Efficient Inference in LLMs A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting

Reference 20

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