Paper Citation Record · LEDGER

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning

As of 21 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2504.14677.

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

pith.paper-citation-record.v1
2504.14677 v1

Coverage vector

measured 33 of 33 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-16T11:47:03.335036Z

measured 33 of 33 standing notices

One-hop event checks from named stored sources.

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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

33 of 33 outbound references displayed

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External citation measurements

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Outbound references

Observation 1c235028-a785-4f76-b53d-7fc624f6035a · outbound

This paper cites Financial time series forecasting with deep learning: A systematic literature review: 2005–2019,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Financial time series forecasting with deep learning: A systematic literature review: 2005–2019,

Reference 1

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Observation 49f3d259-359b-47b2-b10f-cbca45f56b93 · outbound

This paper cites Unsupervised represen- tation learning for time series with temporal neighborhood coding,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Unsupervised represen- tation learning for time series with temporal neighborhood coding,

Reference 2

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Observation 982e9c46-a6fa-499b-a269-ffada3b97a12 · outbound

This paper cites Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Diffusion convolutional recurrent neural network: Data-driven traffic forecasting,

Reference 3

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This paper cites Accurate medium-range global weather forecasting with 3d neural networks,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Accurate medium-range global weather forecasting with 3d neural networks,

Reference 4

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Observation 0d7529d0-68c0-40be-9c33-6a5a6627f17d · outbound

This paper cites Multivariate temporal convolutional network: A deep neural networks approach for multivariate time series forecasting,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Multivariate temporal convolutional network: A deep neural networks approach for multivariate time series forecasting,

Reference 5

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Observation edc2015d-8cf4-41c1-96a4-3d78df5ae1ba · outbound

This paper cites Temporal convolutional neural (tcn) network for an effective weather forecasting using time-series data from the local weather station,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Temporal convolutional neural (tcn) network for an effective weather forecasting using time-series data from the local weather station,

Reference 6

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Observation 64e18d7a-3480-41a5-956b-bd6377386910 · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 7

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Observation 8e2adc5d-1978-4c8d-83b4-2395e06f0c63 · outbound

This paper cites Crossformer: Transformer utilizing cross- dimension dependency for multivariate time series forecasting,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Crossformer: Transformer utilizing cross- dimension dependency for multivariate time series forecasting,

Reference 8

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Observation 083425f6-d35c-4761-89e5-6b871526166c · outbound

This paper cites itrans- former: Inverted transformers are effective for time series forecasting,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning itrans- former: Inverted transformers are effective for time series forecasting,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:47:03.699568Z

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Observation f5ce3603-1910-4dec-8861-168bb789238f · outbound

This paper cites Scaling-laws for Large Time-series Models.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Scaling-laws for Large Time-series Models

Reference 10

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Observation e41474c2-0549-4d4a-8155-263106b52e42 · outbound

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

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Unified Training of Universal Time Series Forecasting Transformers

Reference 11

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Observation 02f8755f-e1cf-4abd-b451-dc26cd775560 · outbound

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

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts

Reference 12

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

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning A decoder-only foundation model for time-series forecasting,

Reference 13

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Observation d512ba83-72fa-471e-b77e-c9e5da0349f3 · outbound

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

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Chronos: Learning the Language of Time Series

Reference 14

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Observation ffcb29a7-461a-4fc2-a875-405ab783f464 · outbound

This paper cites Large language models are zero-shot time series forecasters,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Large language models are zero-shot time series forecasters,

Reference 15

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Observation a76f1e72-f28c-4c14-b981-d2ef18da9ccb · outbound

This paper cites Time series prediction method of industrial process with limited data based on transfer learning,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Time series prediction method of industrial process with limited data based on transfer learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:47:03.665949Z

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Observation baead503-e7e5-4846-addf-2b66aa9b1344 · outbound

This paper cites An adaptive continual learning method for nonstationary industrial time series prediction,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning An adaptive continual learning method for nonstationary industrial time series prediction,

Reference 17

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Observation 9adf85bf-76e4-42e7-8f5b-2b5ba5d91972 · outbound

This paper cites Loss of plasticity in deep continual learning,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Loss of plasticity in deep continual learning,

Reference 18

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This paper cites Catastrophic interference in connec- tionist networks: The sequential learning problem,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Catastrophic interference in connec- tionist networks: The sequential learning problem,

Reference 19

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Observation 6cfabd49-1adc-40bc-b078-cbd41fc5cf74 · outbound

This paper cites A survey of time series foundation models: Generalizing time series representation with large language mode,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning A survey of time series foundation models: Generalizing time series representation with large language mode,

Reference 20

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This paper cites One fits all: Power general time series analysis by pretrained lm,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning One fits all: Power general time series analysis by pretrained lm,

Reference 21

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Observation 633e62ed-6edb-4898-b59b-9da1a28b2afd · outbound

This paper cites Time-LLM: Time series forecasting by reprogramming large language models,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Time-LLM: Time series forecasting by reprogramming large language models,

Reference 22

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Observation c682e2d0-fb40-49d7-b30e-6e66c40ef7d0 · outbound

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

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning AutoTimes: Autoregressive Time Series Forecasters via Large Language Models

Reference 23

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This paper cites A comprehensive survey of continual learning: theory, method and application,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning A comprehensive survey of continual learning: theory, method and application,

Reference 24

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Observation da3eaef5-6d67-4ccb-8110-f24b436c8c98 · outbound

This paper cites The dormant neuron phenomenon in deep reinforcement learning,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning The dormant neuron phenomenon in deep reinforcement learning,

Reference 25

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Observation 4c71497d-b30c-46a6-a62b-1dc2e04b2b28 · outbound

This paper cites Maintaining plasticity in continual learning via regenerative regularization,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Maintaining plasticity in continual learning via regenerative regularization,

Reference 26

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Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Overcoming catas- trophic forgetting with hard attention to the task,

Reference 27

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Observation 0275d730-914a-4e8d-9150-888c6a7a1c13 · outbound

This paper cites icarl: Incremental classifier and representation learning,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning icarl: Incremental classifier and representation learning,

Reference 28

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This paper cites Gradient episodic memory for continual learning,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Gradient episodic memory for continual learning,

Reference 29

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This paper cites Msgnet: Learning multi- scale inter-series correlations for multivariate time series forecasting,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Msgnet: Learning multi- scale inter-series correlations for multivariate time series forecasting,

Reference 30

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This paper cites Rethinking urban mobility prediction: A multivariate time series forecasting approach,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Rethinking urban mobility prediction: A multivariate time series forecasting approach,

Reference 31

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Observation 5b8d87b0-2910-415c-8186-3a40250fb39f · outbound

This paper cites Are transformers effective for time series forecasting?.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning Are transformers effective for time series forecasting?

Reference 32

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Observation d1e6fa4c-ac7d-4253-ba7c-fa27f0aed9c8 · outbound

This paper cites A time series is worth 64 words: Long-term forecasting with transformers,.

Evaluating Temporal Plasticity in Foundation Time Series Models for Incremental Fine-tuning A time series is worth 64 words: Long-term forecasting with transformers,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:47:03.525310Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

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