Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-08T17:06:03.409263Z
Paper Citation Record · LEDGER
Investigating Compositional Reasoning in Time Series Foundation Models
As of 21 August 2026, this Paper Citation Record lists 64 of 64 outbound references and 2 inbound Pith citation observations for arXiv:2502.06037.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-05T17:12:07.198774Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T17:12:07.655636Z
Reference resolution
64 of 64 outbound references displayed
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- verified fuzzy38
- unresolved25
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External citation measurements
No source-named external measurement is stored.
Outbound references
Observation a1b32696-c4bd-48b4-93cb-313ad5699482 · outbound
This paper cites Gift-eval: A benchmark for general time series forecasting model evaluation,.
Investigating Compositional Reasoning in Time Series Foundation Models Gift-eval: A benchmark for general time series forecasting model evaluation,
Reference 1
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 98afaf72-eb2d-4fc2-931e-b17479e0114d · outbound
This paper cites Physics of language models: Part 3.2, knowledge manipula- tion, 2023.
Investigating Compositional Reasoning in Time Series Foundation Models Physics of language models: Part 3.2, knowledge manipula- tion, 2023
Reference 2
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 857c3e0c-dfec-4639-884d-575908406ad9 · outbound
This paper cites Physics of language models: Part 3.1, knowledge storage and extraction.
Investigating Compositional Reasoning in Time Series Foundation Models Physics of language models: Part 3.1, knowledge storage and extraction
Reference 3
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 16a36fe2-6e6e-46db-b328-db07d8f0864a · outbound
This paper cites Maddix, Hao Wang, Michael W.
Investigating Compositional Reasoning in Time Series Foundation Models Maddix, Hao Wang, Michael W
Reference 4
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d1a82976-a269-48d3-9bc1-d0c7699be9b9 · outbound
This paper cites Zico Kolter, and Vladlen Koltun.
Investigating Compositional Reasoning in Time Series Foundation Models Zico Kolter, and Vladlen Koltun
Reference 5
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ea2fa3d7-9cb8-4e52-afc0-8700ce9b99ce · outbound
This paper cites an unresolved cited work.
Investigating Compositional Reasoning in Time Series Foundation Models Unresolved cited work
Reference 6
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 0dec5cd9-0d0b-478d-93d3-0221061e60b1 · outbound
This paper cites TimeSeriesExam: A time series understanding exam.NeurIPS 2024 Workshop on Time Series in the Age of Large Model, 2024.
Investigating Compositional Reasoning in Time Series Foundation Models TimeSeriesExam: A time series understanding exam.NeurIPS 2024 Workshop on Time Series in the Age of Large Model, 2024
Reference 7
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 6bb5eb1b-30f1-4b19-8167-c2e9c118e12e · outbound
This paper cites Olivares, Boris N.
Investigating Compositional Reasoning in Time Series Foundation Models Olivares, Boris N
Reference 8
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 02f17d75-3b3a-42d2-9061-f4b545d26437 · outbound
This paper cites Yoder, Sercan ¨O.
Investigating Compositional Reasoning in Time Series Foundation Models Yoder, Sercan ¨O
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.677152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c68c7734-ce64-4584-860b-8994293ea7f0 · outbound
This paper cites Hallgrímsson, Maxwell A.
Investigating Compositional Reasoning in Time Series Foundation Models Hallgrímsson, Maxwell A
Reference 10
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b8df766e-c2fe-4ede-9d26-b15da12a7c47 · outbound
This paper cites Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction.
Investigating Compositional Reasoning in Time Series Foundation Models Deep Bidirectional and Unidirectional LSTM Recurrent Neural Network for Network-wide Traffic Speed Prediction
Reference 11
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation db6532e5-c953-44c6-864d-714757f61e04 · outbound
This paper cites an unresolved cited work.
Investigating Compositional Reasoning in Time Series Foundation Models Unresolved cited work
Reference 12
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ea9ecc22-829c-4199-8482-fa580ef49a7d · outbound
This paper cites A decoder-only foundation model for time-series forecasting, 2024.
Investigating Compositional Reasoning in Time Series Foundation Models A decoder-only foundation model for time-series forecasting, 2024
Reference 13
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7bc07e62-8bf8-467a-8b1e-cdca080e9cd0 · outbound
This paper cites Statistical comparisons of classifiers over multiple data sets.
Investigating Compositional Reasoning in Time Series Foundation Models Statistical comparisons of classifiers over multiple data sets
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.639092Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d5982cbc-c621-4a23-ade6-42c220f77682 · outbound
This paper cites Scaling-laws for Large Time-series Models.
Investigating Compositional Reasoning in Time Series Foundation Models Scaling-laws for Large Time-series Models
Reference 15
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation adf20b76-d87f-4185-b7cc-717fe7f2df69 · outbound
This paper cites Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series.
Investigating Compositional Reasoning in Time Series Foundation Models Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series
Reference 16
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e82a3bc0-41de-4a24-9ca8-4b814386c62f · outbound
This paper cites Cognitron: A self-organizing multilayered neural network.Biol.
Investigating Compositional Reasoning in Time Series Foundation Models Cognitron: A self-organizing multilayered neural network.Biol
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.627450Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation e650a15c-43bd-4b46-b981-aed8de0efc30 · outbound
This paper cites Units: A unified multi-task time series model.
Investigating Compositional Reasoning in Time Series Foundation Models Units: A unified multi-task time series model
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.615579Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation ee1dde33-2905-420a-846d-445e52396c51 · outbound
This paper cites TimeGPT-1, 2023.
Investigating Compositional Reasoning in Time Series Foundation Models TimeGPT-1, 2023
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.603730Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d20d23d6-c5c1-4dd1-8ae2-5a22508daea8 · outbound
This paper cites Aqua: A benchmarking tool for label quality assessment.Advances in Neural Information Processing Systems, 36, 2024.
Investigating Compositional Reasoning in Time Series Foundation Models Aqua: A benchmarking tool for label quality assessment.Advances in Neural Information Processing Systems, 36, 2024
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.591651Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation f371285d-873e-4340-87c0-a97419dda1cd · outbound
This paper cites MOMENT: A family of open time-series foundation models.
Investigating Compositional Reasoning in Time Series Foundation Models MOMENT: A family of open time-series foundation models
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.579941Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation e605fa67-7a6f-4c74-8d58-000ca2344e0a · outbound
This paper cites How does gpt-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model.
Investigating Compositional Reasoning in Time Series Foundation Models How does gpt-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.546415Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 7d8a18b1-0263-40c5-a1de-48370c9e3066 · outbound
This paper cites an unresolved cited work.
Investigating Compositional Reasoning in Time Series Foundation Models Unresolved cited work
Reference 23
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4e651570-0735-4382-bc10-015a4e807a78 · outbound
This paper cites Olivares.Forecasting: Principles and Practice, the Pythonic Way.
Investigating Compositional Reasoning in Time Series Foundation Models Olivares.Forecasting: Principles and Practice, the Pythonic Way
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.385205Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 71d868dc-62d3-4e76-adf8-1c3ffb847b34 · outbound
This paper cites Deep learning for time series classification: a review.Data Mining and Knowledge Discovery, 33(4):917–963, 2019.
Investigating Compositional Reasoning in Time Series Foundation Models Deep learning for time series classification: a review.Data Mining and Knowledge Discovery, 33(4):917–963, 2019
Reference 25
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 389ca159-6e39-4b86-9fd2-34499c1c3eaa · outbound
This paper cites LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction.
Investigating Compositional Reasoning in Time Series Foundation Models LibCity: A Unified Library Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction
Reference 26
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bf468e61-d532-450d-9c4a-bd01325c7d4f · outbound
This paper cites Lake and Marco Baroni.
Investigating Compositional Reasoning in Time Series Foundation Models Lake and Marco Baroni
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.301436Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4390acbc-cc04-4bdf-946f-ccdaa049be92 · outbound
This paper cites Arık, Nicolas Loeff, and Tomas Pfister.
Investigating Compositional Reasoning in Time Series Foundation Models Arık, Nicolas Loeff, and Tomas Pfister
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.282641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 71d0a4db-9dbc-4b85-b3ba-b19ee61a9352 · outbound
This paper cites Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts.
Investigating Compositional Reasoning in Time Series Foundation Models Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts
Reference 29
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ef9cf9d2-b939-4d0c-bb03-f9f83388ab50 · outbound
This paper cites iTransformer: Inverted transformers are effective for time series forecasting, 2024.
Investigating Compositional Reasoning in Time Series Foundation Models iTransformer: Inverted transformers are effective for time series forecasting, 2024
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.271503Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation b705e6e4-6367-42d6-b68e-072a95bde545 · outbound
This paper cites Timer-XL: Long-Context Transformers for Unified Time Series Forecasting.
Investigating Compositional Reasoning in Time Series Foundation Models Timer-XL: Long-Context Transformers for Unified Time Series Forecasting
Reference 31
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a8303647-c57b-4731-8aaf-6b379417bf94 · outbound
This paper cites Timer: Generative Pre-trained Transformers Are Large Time Series Models.
Investigating Compositional Reasoning in Time Series Foundation Models Timer: Generative Pre-trained Transformers Are Large Time Series Models
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.260608Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 30da234f-5da7-4ce2-a3b7-b93e4570c59f · outbound
This paper cites Merrill, Mingtian Tan, Vinayak Gupta, Tom Hartvigsen, and Tim Althoff.
Investigating Compositional Reasoning in Time Series Foundation Models Merrill, Mingtian Tan, Vinayak Gupta, Tom Hartvigsen, and Tim Althoff
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.249046Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 3adc7f1e-663b-4a95-8877-0640ee4e1607 · outbound
This paper cites Subseasonalclimateusa: A dataset for subseasonal forecasting and benchmarking.
Investigating Compositional Reasoning in Time Series Foundation Models Subseasonalclimateusa: A dataset for subseasonal forecasting and benchmarking
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.238312Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 6dd30b9e-2899-4b13-b36c-2072904801e6 · outbound
This paper cites an unresolved cited work.
Investigating Compositional Reasoning in Time Series Foundation Models Unresolved cited work
Reference 35
- Resolution
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Source-reported events for the cited work
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Observation f25943d2-e7e6-4501-926e-eaa1b55df1e8 · outbound
This paper cites Basisformer: Attention-based time series forecasting with learnable and interpretable basis.
Investigating Compositional Reasoning in Time Series Foundation Models Basisformer: Attention-based time series forecasting with learnable and interpretable basis
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.216991Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a2c0da33-0865-485f-8eaf-f56125b3f090 · outbound
This paper cites Nguyen, and Phanwadee Sinthong an Jayant Kalagnanam2.
Investigating Compositional Reasoning in Time Series Foundation Models Nguyen, and Phanwadee Sinthong an Jayant Kalagnanam2
Reference 37
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.206056Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 5c420e7d-c286-4683-9422-934fcea9ee37 · outbound
This paper cites Olivares, Cristian Challu, Grzegorz Marcjasz, Rafal Weron, and Artur Dubrawski.
Investigating Compositional Reasoning in Time Series Foundation Models Olivares, Cristian Challu, Grzegorz Marcjasz, Rafal Weron, and Artur Dubrawski
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.195152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 5c36a629-73ff-479b-987b-e9d1d3209c23 · outbound
This paper cites Olivares, Cristian Challú, Federico Garza, Max Mergenthaler Canseco, and Artur Dubrawski.
Investigating Compositional Reasoning in Time Series Foundation Models Olivares, Cristian Challú, Federico Garza, Max Mergenthaler Canseco, and Artur Dubrawski
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.184696Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 46b24e28-e436-4d6b-94d4-756d66762f58 · outbound
This paper cites Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio.
Investigating Compositional Reasoning in Time Series Foundation Models Oreshkin, Dmitri Carpov, Nicolas Chapados, and Yoshua Bengio
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.173380Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 81acb33a-06f4-4fc3-9943-165674c04dfc · outbound
This paper cites Lag-Llama: Towards foundation models for probabilistic time series forecasting, 2024.
Investigating Compositional Reasoning in Time Series Foundation Models Lag-Llama: Towards foundation models for probabilistic time series forecasting, 2024
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.151990Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation f94882fb-fde7-4c69-9acf-ee88469f2bb0 · outbound
This paper cites The perceptron: A probabilistic model for information storage and organiza- tion in the brain.Psychological Review, 65(6):386—-408, 1958.
Investigating Compositional Reasoning in Time Series Foundation Models The perceptron: A probabilistic model for information storage and organiza- tion in the brain.Psychological Review, 65(6):386—-408, 1958
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:04.047037Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a290f58f-0857-47a7-9033-dadf72f3b5bc · outbound
This paper cites Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition, 2014.
Investigating Compositional Reasoning in Time Series Foundation Models Long short-term memory based recurrent neural network architectures for large vocabulary speech recognition, 2014
Reference 43
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:03.963898Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 3867702d-9f07-412b-858e-643e0152e4c3 · outbound
This paper cites Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts.
Investigating Compositional Reasoning in Time Series Foundation Models Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts
Reference 44
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T17:06:03.258620Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7159e79a-0ba9-44b6-85e3-378590915e8b · outbound
This paper cites Merrill, Vinayak Gupta, Tim Althoff, and Thomas Hartvigsen.
Investigating Compositional Reasoning in Time Series Foundation Models Merrill, Vinayak Gupta, Tim Althoff, and Thomas Hartvigsen
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:03.864479Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 55b197c1-9867-4b84-8aa3-8c86a7a40297 · outbound
This paper cites WaveNet: A generative model for raw audio, 2016.
Investigating Compositional Reasoning in Time Series Foundation Models WaveNet: A generative model for raw audio, 2016
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:03.846590Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 5a5882b1-2f50-49bf-9155-3ff455d3a5bc · outbound
This paper cites an unresolved cited work.
Investigating Compositional Reasoning in Time Series Foundation Models Unresolved cited work
Reference 47
- Resolution
- unresolved
- raw_fallback, observed 2026-08-08T17:06:03.834501Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 31978ae6-54f5-4c5b-ba5c-6d397d7c61fd · outbound
This paper cites Grokked transformers are implicit reasoners: A mechanistic journey to the edge of generalization.
Investigating Compositional Reasoning in Time Series Foundation Models Grokked transformers are implicit reasoners: A mechanistic journey to the edge of generalization
Reference 48
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:03.822603Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 1c786a2c-ec6c-4baf-b1c1-d965de6e5ed3 · outbound
This paper cites Exploring representations and interventions in time series foundation models.
Investigating Compositional Reasoning in Time Series Foundation Models Exploring representations and interventions in time series foundation models
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:03.811609Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 21990e68-1417-4bfb-bf83-8e849c8cf213 · outbound
This paper cites Context is key: A benchmark for forecasting with essential textual information, 2025.
Investigating Compositional Reasoning in Time Series Foundation Models Context is key: A benchmark for forecasting with essential textual information, 2025
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:03.800222Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d261a187-bc8c-48c0-8d94-43129b996a09 · outbound
This paper cites an unresolved cited work.
Investigating Compositional Reasoning in Time Series Foundation Models Unresolved cited work
Reference 51
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 09540968-0f7a-45f5-8357-21fa463ac0dd · outbound
This paper cites $\spadesuit$ SPADE $\spadesuit$ Split Peak Attention DEcomposition.
Investigating Compositional Reasoning in Time Series Foundation Models $\spadesuit$ SPADE $\spadesuit$ Split Peak Attention DEcomposition
Reference 52
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T17:06:03.347015Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 85422b85-d1cd-4f59-bd7d-208f01608d68 · outbound
This paper cites Unified training of universal time series forecasting transformers.
Investigating Compositional Reasoning in Time Series Foundation Models Unified training of universal time series forecasting transformers
Reference 53
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T17:06:03.362357Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7e103c20-15ab-4028-8028-adc975c5d5ce · outbound
This paper cites Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting, 2021.
Investigating Compositional Reasoning in Time Series Foundation Models Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting, 2021
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:03.771363Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 8738a88c-3f28-4c4b-9008-0354b9558153 · outbound
This paper cites TimesNet: Temporal 2d-variation modeling for general time series analysis.
Investigating Compositional Reasoning in Time Series Foundation Models TimesNet: Temporal 2d-variation modeling for general time series analysis
Reference 55
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:03.759052Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 3bb2ac17-a851-4cdb-b04f-3651e0b4a26a · outbound
This paper cites Rethinking fourier transform from a basis functions perspective for long-term time series forecasting.
Investigating Compositional Reasoning in Time Series Foundation Models Rethinking fourier transform from a basis functions perspective for long-term time series forecasting
Reference 56
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:03.745391Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation c36ab761-5ec1-4de4-b878-52d13683f14e · outbound
This paper cites Do Large Language Models Latently Perform Multi-Hop Reasoning?.
Investigating Compositional Reasoning in Time Series Foundation Models Do Large Language Models Latently Perform Multi-Hop Reasoning?
Reference 57
- Resolution
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ce7faa9b-ffc0-40ee-8a4f-df9dd6e066de · outbound
This paper cites Towards Neural Scaling Laws for Time Series Foundation Models.
Investigating Compositional Reasoning in Time Series Foundation Models Towards Neural Scaling Laws for Time Series Foundation Models
Reference 58
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T17:06:03.389899Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 89ed8ce3-a90f-4f0b-ae19-ff54d5a47818 · outbound
This paper cites Are transformers effective for time series forecasting? InProceedings of the AAAI Conference on Artificial Intelligence, 2023.
Investigating Compositional Reasoning in Time Series Foundation Models Are transformers effective for time series forecasting? InProceedings of the AAAI Conference on Artificial Intelligence, 2023
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T17:06:03.393818Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c1cb1497-6c81-4009-ae7d-df98d0ca0b38 · outbound
This paper cites MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions.
Investigating Compositional Reasoning in Time Series Foundation Models MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions
Reference 60
- Resolution
- unresolved
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Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a5adea6e-774a-4d56-b232-591115c76b6e · outbound
This paper cites Informer: Beyond efficient transformer for long sequence time-series forecasting, 2021.
Investigating Compositional Reasoning in Time Series Foundation Models Informer: Beyond efficient transformer for long sequence time-series forecasting, 2021
Reference 61
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T17:06:03.401372Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c1f47286-2dbd-4fb0-9fb6-ebc6052c0c27 · outbound
This paper cites Pre-trained large language models use fourier features to compute addition, 2024.
Investigating Compositional Reasoning in Time Series Foundation Models Pre-trained large language models use fourier features to compute addition, 2024
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-08T17:06:03.615887Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 52db3298-d1f1-473d-b55c-3fd11b2cd7bb · outbound
This paper cites Towards Long-Context Time Series Foundation Models.
Investigating Compositional Reasoning in Time Series Foundation Models Towards Long-Context Time Series Foundation Models
Reference 63
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-08T17:06:03.409263Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f4b05afa-7913-4e54-849b-ab595100d1b7 · outbound
This paper cites GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation.
Investigating Compositional Reasoning in Time Series Foundation Models GIFT-Eval: A Benchmark For General Time Series Forecasting Model Evaluation
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T17:06:02.948463Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
Observation 585205d3-849f-4971-9d79-5f007035191f · inbound
Cross-device Zero-shot Label Transfer via Alignment of Time Series Foundation Model Embeddings cites this paper.
Cross-device Zero-shot Label Transfer via Alignment of Time Series Foundation Model Embeddings Investigating Compositional Reasoning in Time Series Foundation Models
Reference 9
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T17:12:07.663014Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation 4a0108e0-a14e-4f95-b2bd-b17f00667905 · inbound
A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models cites this paper.
A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models Investigating Compositional Reasoning in Time Series Foundation Models
Reference 88
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T16:49:32.617898Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.