Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-04T13:23:10.074829Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 2 inbound Pith citation observations for arXiv:2510.00809.
A citation records a reference. It does not transfer a finding from one paper to another.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-04T13:23:10.074829Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T15:40:46.345163Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
23 of 23 outbound references displayed
External citation measurements
0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Observation bbe2f3fb-cbd3-4000-9ddf-6785fb67971f · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Language models are unsupervised multitask learners.OpenAI Blog, 1(8):9, 2019
Reference 1
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Observation 6c228c21-5fa6-4427-ba3d-42ef8adffaba · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Bert: Pre-training of deep bidirectional transformers for language understanding
Reference 2
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 15d9661d-a337-4fdc-a5b7-02e1e682ca31 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Learning transferable visual models from natural language supervision
Reference 3
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Observation 467e084e-0e63-4f34-9988-c16dfd54c0c4 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting CoCa: Contrastive Captioners are Image-Text Foundation Models
Reference 4
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Unavailable: canonical work link unavailable.
Observation eee48fef-49e3-4568-988c-a1b36bd1410d · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Foundation models: A new paradigm for artificial intelligence.Business & Information Systems Engineering, 66(2):221–231, 2024
Reference 5
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Unavailable: canonical work link unavailable.
Observation 4a68239a-8509-4f66-a8ad-270a8f5e35fc · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Effect of model and pretraining scale on catastrophic forgetting in neural networks
Reference 6
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Unavailable: canonical work link unavailable.
Observation 42806fc2-285b-4d12-9c78-ea6eab8a6046 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
Reference 7
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Unavailable: canonical work link unavailable.
Observation 65f580da-ea13-4c1c-b467-67c3fd8a5494 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Catastrophic Forgetting in LLMs: A Comparative Analysis Across Language Tasks
Reference 8
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3b773a3f-8a38-45a8-9353-93a35702f1e0 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting A decoder-only foundation model for time-series forecasting
Reference 9
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Unavailable: canonical work link unavailable.
Observation bd76e878-42e6-43d7-b768-911abd056197 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Attention is all you need
Reference 10
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation cab70a6c-a31f-433f-af7a-4ea163687293 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Large language models are zero-shot time series forecasters
Reference 11
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Unavailable: canonical work link unavailable.
Observation ca2e8b85-23f2-4946-af09-821ae791428e · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Are Transformers Effective for Time Series Forecasting?
Reference 12
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Unavailable: canonical work link unavailable.
Observation 9e12d2ba-7a71-47fd-b471-d1cf1e23fb40 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Informer: Beyond efficient transformer for long sequence time-series forecasting
Reference 13
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Unavailable: canonical work link unavailable.
Observation ae700d7c-05ce-4e0e-b819-65f0b7ad7e07 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting The stability-plasticity dilemma: in- vestigating the continuum from catastrophic forgetting to age-limited learning effects.Frontiers in Psychology, 4:504, 2013
Reference 14
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 65126e86-9f28-4e2b-a26e-07b8c213ae71 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Foundation models for time series analysis: A tutorial and survey
Reference 15
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Unavailable: canonical work link unavailable.
Observation 86641115-cf77-438b-90dc-1c9934cbcf59 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting A Survey of Deep Learning and Foundation Models for Time Series Forecasting
Reference 16
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Observation 14ac2c0e-190a-4f0f-a57d-df64a42472f3 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting TimeGPT-1
Reference 17
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Unavailable: canonical work link unavailable.
Observation 571db03e-b7cf-4819-ad02-4b8f49552908 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting A time series is worth 64 words: Long-term forecasting with transformers
Reference 18
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Unavailable: canonical work link unavailable.
Observation a5854c46-4fd0-49b2-afa7-d3dad78273ab · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting FEDformer: Frequency enhanced decomposed transformer for long-term series forecasting
Reference 19
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Unavailable: canonical work link unavailable.
Observation 2869438a-4bf0-474f-a6e1-f75fc2c83af3 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting GIFT-Eval: A Benchmark for General Time Series Forecasting Model Evaluation
Reference 20
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Observation a28cd21c-849a-4421-8acd-aeac76627858 · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting OpenTS – A Comprehensive and Fair Benchmark for Time Series An- alytics
Reference 21
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Observation a75aa5e0-eb39-4a3d-a271-423d903239df · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Foundation time series arena – benchmarking foundation models for time series
Reference 22
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Observation b7f82544-23f0-49d2-9820-eef66a99ae5b · outbound
Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting Beyond not-forgetting: Continual learning with backward knowledge transfer
Reference 23
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Observation e04cb43c-b8bf-464e-8db0-b6663c7a9660 · inbound
FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting
Reference 75
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation b4f915af-0049-4c4c-9acc-1fb465520282 · inbound
FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting Foundation vs. Specialized Models: Evaluating Catastrophic Forgetting in Continual Time Series Forecasting
Reference 75
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Unavailable: canonical work link unavailable.