Paper Citation Record · LEDGER

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting

As of 9 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2502.10235.

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

pith.paper-citation-record.v1
2502.10235 v1

Coverage vector

measured 51 of 51 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T18:59:00.032824Z

measured 52 of 52 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T16:49:19.112440Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T16:50:10.927536Z

Reference resolution

51 of 51 outbound references displayed

verified exact1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation a68edac5-7b9a-47ef-9bf5-fe50080e7f1f · outbound

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

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Chronos: Learning the Language of Time Series

Reference 1

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This paper cites an unresolved cited work.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Unresolved cited work

Reference 2

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Observation de3671ba-f2af-45af-a12c-658d4f3bbf12 · outbound

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

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Accurate medium-range global weather forecasting with 3d neural networks

Reference 3

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Observation e6a48999-203f-46ea-bebe-b44d5cadcd3c · outbound

This paper cites O., Yoder, N.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting O., Yoder, N

Reference 4

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This paper cites S tochastic G radient H amiltonian M onte C arlo.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting S tochastic G radient H amiltonian M onte C arlo

Reference 5

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Observation 0ccf97af-057c-4e15-aac9-f7b6ac155525 · outbound

This paper cites Hebo: Pushing the limits of sample-efficient hyperparameter optimisation.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Hebo: Pushing the limits of sample-efficient hyperparameter optimisation

Reference 6

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Observation 233fdb02-5c4d-48c4-a24d-65931ae135ff · outbound

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

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting A decoder-only foundation model for time-series forecasting

Reference 7

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Observation 30734d04-4604-44f1-9958-b060167d173b · outbound

This paper cites Density estimation using Real NVP.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Density estimation using Real NVP

Reference 8

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Observation 20a804fc-c44f-48d4-9263-753127e1c305 · outbound

This paper cites User-friendly Foundation Model Adapters for Multivariate Time Series Classification.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting User-friendly Foundation Model Adapters for Multivariate Time Series Classification

Reference 9

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Observation cd5ea04d-957d-41db-92c4-aa8451968626 · outbound

This paper cites and Prevention.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting and Prevention

Reference 10

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Observation 61da3646-4d88-4bce-a177-7c39d4f283fb · outbound

This paper cites and Ghahramani, Z.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting and Ghahramani, Z

Reference 11

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Observation 5121ea11-2b24-4063-a274-28a3116ae301 · outbound

This paper cites Bayesian Data Analysis.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Bayesian Data Analysis

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:01.258467Z

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Observation 26eeef38-b2dd-4574-9f1c-6117fa0595b3 · outbound

This paper cites and Katzfuss, M.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting and Katzfuss, M

Reference 13

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Observation 812064c3-497a-4553-abfe-5cee5ae24242 · outbound

This paper cites Moment: A family of open time-series foundation models.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Moment: A family of open time-series foundation models

Reference 14

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Observation a369ec40-3e97-4f55-8ef1-a57d34e847ed · outbound

This paper cites Practical variational inference for neural networks.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Practical variational inference for neural networks

Reference 15

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Observation 42f8a995-2eda-4d5c-b9b0-6a05ca17d3b3 · outbound

This paper cites J., Paap, R., and Ravazzolo, F.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting J., Paap, R., and Ravazzolo, F

Reference 16

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Observation 97e3e7cb-3a23-4399-9bf2-6993aa6a97be · outbound

This paper cites beta- VAE : Learning basic visual concepts with a constrained variational framework.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting beta- VAE : Learning basic visual concepts with a constrained variational framework

Reference 17

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Observation 235fb9a6-c476-4baa-b0ea-6830d5c1c7a8 · outbound

This paper cites Samformer: Unlocking the potential of transformers in time series forecasting with sharpness-aware minimization and channel-wise attention.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Samformer: Unlocking the potential of transformers in time series forecasting with sharpness-aware minimization and channel-wise attention

Reference 18

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Observation cefbfc62-7a6d-4242-b14f-3b2a073fe303 · outbound

This paper cites an unresolved cited work.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Unresolved cited work

Reference 19

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Observation 9d1d96f3-7f42-4370-8da8-2e4e9e02ef6c · outbound

This paper cites Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-F., Pan, S., and Wen, Q.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Y., Shi, X., Chen, P.-Y., Liang, Y., Li, Y.-F., Pan, S., and Wen, Q

Reference 20

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Observation 05376f04-1f2f-4e42-aa4b-a2c06bb01809 · outbound

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AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Unresolved cited work

Reference 21

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Observation b9879241-c0d6-47c7-ad03-cecbb5512b76 · outbound

This paper cites Reversible instance normalization for accurate time-series forecasting against distribution shift.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Reversible instance normalization for accurate time-series forecasting against distribution shift

Reference 22

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Observation dcb4f7b1-8217-4a33-8186-0560e1985bf0 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Adam: A Method for Stochastic Optimization

Reference 23

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This paper cites Auto-Encoding Variational Bayes.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Auto-Encoding Variational Bayes

Reference 24

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This paper cites J., and Brubaker, M.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting J., and Brubaker, M

Reference 25

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Observation a06eaa2d-42b0-4800-b79b-5d047e0a9f3b · outbound

This paper cites Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Reference 26

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AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Unresolved cited work

Reference 27

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Observation 05edc3df-8ce5-4318-9891-9ffdca0a7aa2 · outbound

This paper cites Padapter: Adapter combined with prompt for image and video classification.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Padapter: Adapter combined with prompt for image and video classification

Reference 28

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Observation dc5f848f-f054-45ec-98df-3ae43653af33 · outbound

This paper cites Tune: A Research Platform for Distributed Model Selection and Training.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Tune: A Research Platform for Distributed Model Selection and Training

Reference 29

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This paper cites Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts

Reference 30

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AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting C., Golestan, K., Yu, G., Volkovs, M., and Caterini, A

Reference 31

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This paper cites H., Sinthong, P., and Kalagnanam, J.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting H., Sinthong, P., and Kalagnanam, J

Reference 32

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Observation 41b525ea-482c-4b94-8b91-f7e873d5513e · outbound

This paper cites and Weron, R.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting and Weron, R

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:01.010157Z

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Observation 616229cf-62b1-4c32-a0ac-37532b442e45 · outbound

This paper cites Towards the probabilistic earth-system simulator: A vision for the future of climate and weather prediction.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Towards the probabilistic earth-system simulator: A vision for the future of climate and weather prediction

Reference 34

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Observation faf1eecc-42a8-493e-8783-20ef0e9f4aaa · outbound

This paper cites St-adapter: Parameter-efficient image-to-video transfer learning.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting St-adapter: Parameter-efficient image-to-video transfer learning

Reference 35

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Observation b45fd52a-f1bd-4cd2-9ad6-8189a35720e8 · outbound

This paper cites M., Hubin, A., Immer, A., Karaletsos, T., Khan, M.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting M., Hubin, A., Immer, A., Karaletsos, T., Khan, M

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:00.943285Z

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Observation 82a2a701-c53b-40cd-b119-6ed089a9e086 · outbound

This paper cites R., Ghonia, H., Bhagwatkar, R., Khorasani, A., Bayazi, M.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting R., Ghonia, H., Bhagwatkar, R., Khorasani, A., Bayazi, M

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:00.922856Z

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Observation 0d47d09c-66ba-4f63-b1dc-58ddcb790385 · outbound

This paper cites Do B ayesian neural networks need to be fully stochastic? In Ruiz, F., Dy, J., and van de Meent, J.-W.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Do B ayesian neural networks need to be fully stochastic? In Ruiz, F., Dy, J., and van de Meent, J.-W

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:00.904666Z

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Observation e9285ca2-7a3f-4afe-b4ad-3a8b578e0a39 · outbound

This paper cites Dropout: A simple way to prevent neural networks from overfitting.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Dropout: A simple way to prevent neural networks from overfitting

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:00.884537Z

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Observation 4da402fc-e48e-4b99-a1ae-b2bcca7a0372 · outbound

This paper cites One Fits All : Power general time series analysis by pretrained lm.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting One Fits All : Power general time series analysis by pretrained lm

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:00.858925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 95b7233c-90c0-4a0c-8c15-7188a6bb0e62 · outbound

This paper cites All you need is a good functional prior for B ayesian deep learning.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting All you need is a good functional prior for B ayesian deep learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:00.840572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c7cbe49a-20dd-4dc3-aacd-8b14516c4446 · outbound

This paper cites an unresolved cited work.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Unresolved cited work

Reference 42

Resolution: unresolved; raw_fallback, observed 2026-08-07T18:59:00.821563Z

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No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 3994d753-db5d-4560-9d22-49429ad41fa6 · outbound

This paper cites Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:00.802612Z

Source-reported events for the cited work

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Observation a1561529-be7d-4974-add4-00e5e3be3dbc · outbound

This paper cites X., Robeyns, M., Wang, X., and Aitchison, L.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting X., Robeyns, M., Wang, X., and Aitchison, L

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:00.778579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8a9ac2a7-4231-43cd-b7fc-868d50dcca93 · outbound

This paper cites Adapter is All You Need for Tuning Visual Tasks.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Adapter is All You Need for Tuning Visual Tasks

Reference 45

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Source-reported events for the cited work

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Observation 72cce338-a13c-457f-ab03-73a2cf65baa1 · outbound

This paper cites Are transformers effective for time series forecasting? 2023.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Are transformers effective for time series forecasting? 2023

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:00.759456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ae67f923-2d15-4c5f-9bdf-67ce7baa7452 · outbound

This paper cites MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T18:59:00.011129Z

Source-reported events for the cited work

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Observation a6f638d4-0545-47bc-8d70-c033039641a5 · outbound

This paper cites and Yan, J.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting and Yan, J

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:00.740542Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 00ddb1b8-d27e-4d66-9cfc-b19b0f075981 · outbound

This paper cites Review on probabilistic forecasting of wind power generation.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Review on probabilistic forecasting of wind power generation

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:00.722007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation c10c0505-9f56-4855-8486-5c67cc0e8dd8 · outbound

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

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T18:59:00.703196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 23a48789-55d0-454d-89ff-6f0089dde3ac · outbound

This paper cites write newline.

AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting write newline

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-07T18:59:00.032824Z

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Pith citing papers

Observation 1c6fb927-687a-443f-9b10-2f96123197ee · inbound

Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling cites this paper.

Timer-S1: A Billion-Scale Time Series Foundation Model with Serial Scaling AdaPTS: Adapting Univariate Foundation Models to Probabilistic Multivariate Time Series Forecasting

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:50:10.931522Z

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