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Paper Citation Record · LEDGER

LightGTS: A Lightweight General Time Series Forecasting Model

As of 8 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2506.06005.

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

pith.paper-citation-record.v1
2506.06005 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:08:58.023490Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T10:58:02.534124Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T10:58:02.786090Z

Reference resolution

30 of 30 outbound references displayed

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External citation measurements

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Outbound references

Observation 5a8cd51d-7362-4c0c-a02a-06fa1aa1acd0 · outbound

This paper cites Chronos: Learning the Language of Time Series.

LightGTS: A Lightweight General Time Series Forecasting Model Chronos: Learning the Language of Time Series

Reference 1

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Observation 415598c2-a630-4763-a838-f4a9641eaf36 · outbound

This paper cites Monash Time Series Forecasting Archive.

LightGTS: A Lightweight General Time Series Forecasting Model Monash Time Series Forecasting Archive

Reference 5

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Observation a8c8250a-184a-4bcc-a2d8-c1f6fb25b60d · outbound

This paper cites Non-Autoregressive Neural Machine Translation.

LightGTS: A Lightweight General Time Series Forecasting Model Non-Autoregressive Neural Machine Translation

Reference 6

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Observation cb694c29-870c-47ea-a78d-d9935bea514e · outbound

This paper cites Adam: A Method for Stochastic Optimization.

LightGTS: A Lightweight General Time Series Forecasting Model Adam: A Method for Stochastic Optimization

Reference 8

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Observation 888d25ff-4387-4972-af88-e5f8f153774e · outbound

This paper cites an unresolved cited work.

LightGTS: A Lightweight General Time Series Forecasting Model Unresolved cited work

Reference 9

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Observation 4196b6fd-bb3c-4dc5-b7d2-4b7fe8b58ee3 · outbound

This paper cites Drop Last.

LightGTS: A Lightweight General Time Series Forecasting Model Drop Last

Reference 10

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Observation 0f747497-b083-4f93-b5f2-de67a1f36fa4 · outbound

This paper cites an unresolved cited work.

LightGTS: A Lightweight General Time Series Forecasting Model Unresolved cited work

Reference 12

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Observation f02b5d37-1d10-48d7-9756-3afdba3316ee · outbound

This paper cites Lower MSE or MAE values indicate better predictions.

LightGTS: A Lightweight General Time Series Forecasting Model Lower MSE or MAE values indicate better predictions

Reference 13

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Observation ca5bedd7-38d3-45a4-ab2d-cce4b69cce62 · outbound

This paper cites Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.

LightGTS: A Lightweight General Time Series Forecasting Model Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 14

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Observation 8a088207-c675-4706-a7d2-26ed79d19756 · outbound

This paper cites FITS: Modeling Time Series with $10k$ Parameters.

LightGTS: A Lightweight General Time Series Forecasting Model FITS: Modeling Time Series with $10k$ Parameters

Reference 17

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Observation 660fec47-1303-4d74-9142-6cd2a4978fa3 · outbound

This paper cites Implementation Details A.1.

LightGTS: A Lightweight General Time Series Forecasting Model Implementation Details A.1

Reference 18

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 3a77747b-39d9-4895-b5f9-b3559ae892bd · outbound

This paper cites The complete list of pre-training datasets is shown in Table.

LightGTS: A Lightweight General Time Series Forecasting Model The complete list of pre-training datasets is shown in Table

Reference 19

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Observation 8376a1f6-5ddf-4f91-a92d-51197caa10a6 · outbound

This paper cites an unresolved cited work.

LightGTS: A Lightweight General Time Series Forecasting Model Unresolved cited work

Reference 20

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Observation 85e7d1c4-2e2d-45ae-9c89-eb475733d87a · outbound

This paper cites an unresolved cited work.

LightGTS: A Lightweight General Time Series Forecasting Model Unresolved cited work

Reference 21

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Observation a60abfb7-332b-4257-9a1d-a2eb206d7c14 · outbound

This paper cites Electricity 2 contains the electricity consumption of 321 customers from July 2016 to July 2019, recorded hourly.

LightGTS: A Lightweight General Time Series Forecasting Model Electricity 2 contains the electricity consumption of 321 customers from July 2016 to July 2019, recorded hourly

Reference 22

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation b8434320-2ef4-415c-abe6-8b601be8fc90 · outbound

This paper cites an unresolved cited work.

LightGTS: A Lightweight General Time Series Forecasting Model Unresolved cited work

Reference 23

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 0205fe97-b61b-4ef1-8c47-2492789bef03 · outbound

This paper cites Models Encoder LayersDecoder LayersModel Dim.FFN Dim.Parameters LightGTS-tiny 1 1 256 512 1.3M LightGTS-mini 3 3 256 512 4M B.

LightGTS: A Lightweight General Time Series Forecasting Model Models Encoder LayersDecoder LayersModel Dim.FFN Dim.Parameters LightGTS-tiny 1 1 256 512 1.3M LightGTS-mini 3 3 256 512 4M B

Reference 26

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Observation ce711bfa-6614-48fd-9de3-a45b17df2c82 · outbound

This paper cites The SOTA baselines refer to the best-performing baseline results for each dataset.𝐹𝑆 means the seasonality strength for each dataset.

LightGTS: A Lightweight General Time Series Forecasting Model The SOTA baselines refer to the best-performing baseline results for each dataset.𝐹𝑆 means the seasonality strength for each dataset

Reference 27

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation cd8c97a4-d8d1-47d4-9877-627f16bb87fe · outbound

This paper cites Lower MSE or MAE values indicate better predictions.

LightGTS: A Lightweight General Time Series Forecasting Model Lower MSE or MAE values indicate better predictions

Reference 29

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation f6fc4136-6d21-4862-9d89-9c7dc4a522c2 · outbound

This paper cites Unified Training of Universal Time Series Forecasting Transformers.

LightGTS: A Lightweight General Time Series Forecasting Model Unified Training of Universal Time Series Forecasting Transformers

Reference 2012

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Observation 92cd1237-38ba-4e69-8539-9be3136f0258 · outbound

This paper cites Crafting papers on machine learning.

LightGTS: A Lightweight General Time Series Forecasting Model Crafting papers on machine learning

Reference 2014

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Observation 1e1e2e2e-7fe1-4421-bc4f-41b27a3d0dbb · outbound

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

LightGTS: A Lightweight General Time Series Forecasting Model A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 2016

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Observation 28469c31-4a9e-49d4-8aff-007b0bbcfb78 · outbound

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

LightGTS: A Lightweight General Time Series Forecasting Model Time-LLM: Time Series Forecasting by Reprogramming Large Language Models

Reference 2017

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Observation 39a684c2-823a-4a75-8a7c-d7c0fa0b04f7 · outbound

This paper cites The UEA multivariate time series classification archive, 2018.

LightGTS: A Lightweight General Time Series Forecasting Model The UEA multivariate time series classification archive, 2018

Reference 2018

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Observation ce0a5da6-6004-48fd-aa9a-b589f1888a91 · outbound

This paper cites UniTS: A Unified Multi-Task Time Series Model.

LightGTS: A Lightweight General Time Series Forecasting Model UniTS: A Unified Multi-Task Time Series Model

Reference 2019

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Observation e6851120-d9f6-4243-89f8-8b3fde62f732 · outbound

This paper cites an unresolved cited work.

LightGTS: A Lightweight General Time Series Forecasting Model Unresolved cited work

Reference 2021

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 68c5a3f2-f300-4e96-9028-34e492031a19 · outbound

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

LightGTS: A Lightweight General Time Series Forecasting Model iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 2022

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Observation 0d04fee3-13b7-493d-8cb0-7e4340373bee · outbound

This paper cites Timer: Generative Pre-trained Transformers Are Large Time Series Models.

LightGTS: A Lightweight General Time Series Forecasting Model Timer: Generative Pre-trained Transformers Are Large Time Series Models

Reference 2023

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Observation 0d97fd3e-9d05-4ff8-b52f-df65283142c1 · outbound

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

LightGTS: A Lightweight General Time Series Forecasting Model A decoder-only foundation model for time-series forecasting

Reference 2024

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Observation d464a0aa-2f7a-4cf4-923c-85d32e95bb51 · outbound

This paper cites CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns.

LightGTS: A Lightweight General Time Series Forecasting Model CycleNet: Enhancing Time Series Forecasting through Modeling Periodic Patterns

Reference 2025

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Pith citing papers

Observation d157a3c0-191e-4e34-9faa-c8385b967ade · inbound

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality cites this paper.

Towards Measuring and Modeling Geometric Structures in Time Series Forecasting via Image Modality LightGTS: A Lightweight General Time Series Forecasting Model

Reference 28

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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