Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection | Paper Citation Record

Observation e27c98ce-8d06-4b75-a44d-eb009c6f0e24 · outbound

This paper cites Deep time series forecasting models: A comprehensive survey.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Deep time series forecasting models: A comprehensive survey

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:36.299688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8389eaab-2895-44f6-a139-486a4045aa12 · outbound

This paper cites Deep learning for time series classification and extrinsic regression: A current survey.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Deep learning for time series classification and extrinsic regression: A current survey

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:36.136302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dc8397c1-4067-4d3d-8bcf-6a03d65ef280 · outbound

This paper cites Review of data imputation techniques in time series data: Comparative analysis.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Review of data imputation techniques in time series data: Comparative analysis

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:35.976892Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5d3905ee-7d83-4468-a7be-7138a9ae16b6 · outbound

This paper cites Optimal multi-scale patterns in time series streams.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Optimal multi-scale patterns in time series streams

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:35.797761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e445825f-41c0-4491-8cb0-48cdf91addc4 · outbound

This paper cites Statstream: Statistical monitoring of thousands of data streams in real time.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Statstream: Statistical monitoring of thousands of data streams in real time

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:35.586036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a4963282-088c-47e1-bd27-80eb8881e540 · outbound

This paper cites Transformer-based models to deal with heterogeneous environments in human activity recognition.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Transformer-based models to deal with heterogeneous environments in human activity recognition

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:35.307795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 3e2b6135-adce-4b15-9443-68fe06596882 · outbound

This paper cites Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 2001.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Gradient flow in recurrent nets: the difficulty of learning long-term dependencies, 2001

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:34.582507Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4f9bfb39-b69a-499b-939c-271ed96bc188 · outbound

This paper cites Attention is all you need.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Attention is all you need

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:26.495163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbca421f-eab3-4d9f-9d0c-eccd33c13683 · outbound

This paper cites Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Deep Transformer Models for Time Series Forecasting: The Influenza Prevalence Case

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:26.505039Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb352ed4-e573-4285-9b5c-22de748bee35 · outbound

This paper cites Music Transformer.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Music Transformer

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:26.513492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 980b5a02-cd80-4764-9ec3-bb50363f6291 · outbound

This paper cites A time-restricted self-attention layer for asr.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection A time-restricted self-attention layer for asr

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:33.742382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 9a209841-e7ba-4f4f-aa34-b15f133ccd6e · outbound

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

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:26.528830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85cd8071-4605-465f-9a5a-ebd8c4ff106c · outbound

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

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:33.011721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f0f7abe3-4ede-418c-a72f-a1c25478ef04 · outbound

This paper cites Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:32.487295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5e1330cf-a211-44c6-a993-24bf88ca64f2 · outbound

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

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:26.591491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 75f631c2-5af3-46b1-9621-f194935f4fcd · outbound

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

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:26.678363Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d20922b3-e862-4bb1-b91e-39c13e33f927 · outbound

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

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection iTransformer: Inverted Transformers Are Effective for Time Series Forecasting

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:26.754965Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a7ee344-a097-4e61-ad2a-1bf73fa2eeba · outbound

This paper cites Pathformer: Multi-scale transformers with adaptive pathways for time series forecasting.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Pathformer: Multi-scale transformers with adaptive pathways for time series forecasting

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:32.260390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8fa7cabb-7d79-4551-9126-62e5c4fac188 · outbound

This paper cites TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:26.855475Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff4e8da5-f97f-4ba3-bdbe-4cbaad041846 · outbound

This paper cites Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121–11128, 2023.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Are transformers effective for time series forecasting? In Proceedings of the AAAI conference on artificial intelligence, volume 37, pages 11121–11128, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:32.108684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e1f54803-384b-4814-a7f0-496e5e54ebe3 · outbound

This paper cites Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:26.975007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 837e59c6-8a74-445d-bd44-33509dd15751 · outbound

This paper cites TSMixer: An all-MLP architecture for time series forecast-ing.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection TSMixer: An all-MLP architecture for time series forecast-ing

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:31.957234Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 12d73c20-812c-4b9e-8c8b-3e180d909439 · outbound

This paper cites Cyclenet: Enhancing time series forecasting through modeling periodic patterns.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Cyclenet: Enhancing time series forecasting through modeling periodic patterns

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:31.787048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 686d7b8d-4174-42bf-818e-248475d25d08 · outbound

This paper cites Timecnn: Refining cross-variable interaction on time point for time series forecasting.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Timecnn: Refining cross-variable interaction on time point for time series forecasting

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:31.629488Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fb189637-2e0c-4bc9-8352-538f002bf400 · outbound

This paper cites Efficiently Modeling Long Sequences with Structured State Spaces.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Efficiently Modeling Long Sequences with Structured State Spaces

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:27.239289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43efd2f1-4cc4-470b-93d8-4c2820d0acab · outbound

This paper cites Timemachine: A time series is worth 4 mambas for long-term forecasting.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Timemachine: A time series is worth 4 mambas for long-term forecasting

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:31.433310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 635a86be-ef1d-44c2-9fd0-09f23b83aac5 · outbound

This paper cites Self-supervised contrastive pre-training for time series via time-frequency consistency.Advances in Neural Information Processing Systems, 35:3988–4003, 2022.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Self-supervised contrastive pre-training for time series via time-frequency consistency.Advances in Neural Information Processing Systems, 35:3988–4003, 2022

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:31.298416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4e555dd1-b4f5-42e5-9ee1-4ad5725d9305 · outbound

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

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection One fits all: Power general time series analysis by pretrained lm

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:31.137676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 05b76787-a1bc-4fea-9396-474a3083885f · outbound

This paper cites Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:27.490990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45fd007d-7d0f-496b-983a-7c4e422076f5 · outbound

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

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection MOMENT: A family of open time-series foundation models

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:30.967568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 476eed3b-92c7-4010-8c31-85e9a36b0dff · outbound

This paper cites Unified training of universal time series forecasting transformers.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Unified training of universal time series forecasting transformers

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:27.604158Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c84c8c1-6b54-41ba-87a8-7f313164da8f · outbound

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

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection A decoder-only foundation model for time-series forecasting

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:27.680553Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01d922fb-50f1-4e26-821b-41dde03aa729 · outbound

This paper cites Addressing prediction delays in time series forecasting: A continuous gru approach with derivative regularization.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Addressing prediction delays in time series forecasting: A continuous gru approach with derivative regularization

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:30.796970Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 99b80e24-d322-4a98-b4a2-6b892fe496e9 · outbound

This paper cites TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection TSRM: A Lightweight Temporal Feature Encoding Architecture for Time Series Forecasting and Imputation

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:21:28.808648Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e15d961c-fa28-424b-b692-a0d92884c899 · outbound

This paper cites Mlp-mixer: An all-mlp architecture for vision.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Mlp-mixer: An all-mlp architecture for vision

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:27.876654Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 76c9b0ea-cfec-40c5-bcb0-188802c47bcd · outbound

This paper cites Adversarial sparse transformer for time series forecasting.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Adversarial sparse transformer for time series forecasting

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:30.623777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fe73e4e9-97c4-4a67-9f72-f64a9c422326 · outbound

This paper cites Are Transformers Effective for Time Series Forecasting?.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Are Transformers Effective for Time Series Forecasting?

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:28.045870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 552d90a0-241a-4c70-ba71-0e8d2b051682 · outbound

This paper cites Uci machine learning repository.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Uci machine learning repository

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:28.153208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e3b33ad-7c97-4465-aed1-91ba8c2cbc9a · outbound

This paper cites http://pems.dot.ca.gov.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection http://pems.dot.ca.gov

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:30.480021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d995367c-5495-48bc-9bec-f8ab0aa7bcb9 · outbound

This paper cites Zhang, and JUN ZHOU.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Zhang, and JUN ZHOU

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:30.337123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 62386fc6-bd72-4908-98fc-466ba0a9d42d · outbound

This paper cites TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection TimeMachine: A Time Series is Worth 4 Mambas for Long-term Forecasting

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:28.488280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 57247971-f0eb-4ce4-951b-f013696c1186 · outbound

This paper cites No free lunch theorems for optimization.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection No free lunch theorems for optimization

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:28.538764Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c204b015-fc0a-40a7-9b9e-20b750bc9a17 · outbound

This paper cites https://cbergmeir.com/talks/neurips2024/.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection https://cbergmeir.com/talks/neurips2024/

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:30.170097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 42b4bf2e-b14f-4832-9c96-ba2eaaa6ce52 · outbound

This paper cites Transferability in Deep Learning: A Survey.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Transferability in Deep Learning: A Survey

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:28.546983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d718a361-3e6d-42d1-ab3a-b4ce132be01b · outbound

This paper cites Simmtm: A simple pre- training framework for masked time-series modeling.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Simmtm: A simple pre- training framework for masked time-series modeling

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:30.026006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6357051e-d5b4-429a-88ce-d39b4029544b · outbound

This paper cites HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection HiMTM: Hierarchical Multi-Scale Masked Time Series Modeling with Self-Distillation for Long-Term Forecasting

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:28.555013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86b1aff6-c5b9-49cb-b413-f59529a89490 · outbound

This paper cites CoST: Contrastive learning of disentangled seasonal-trend representations for time series forecasting.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection CoST: Contrastive learning of disentangled seasonal-trend representations for time series forecasting

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:29.841633Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a7a8751a-e0d5-461c-a139-3d49db096e92 · outbound

This paper cites Learning to embed time series patches independently.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Learning to embed time series patches independently

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:29.641403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dd6ef54e-b6ab-4782-b21b-f83dda8aa1a1 · outbound

This paper cites On the Opportunities and Risks of Foundation Models.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection On the Opportunities and Risks of Foundation Models

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:28.566935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01c5cb2b-8b8c-4e43-8a28-38c8794755d9 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:28.570730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5580172a-c481-4b22-bc9b-a1fa4c8b2501 · outbound

This paper cites Efficiently modeling long sequences with structured state spaces.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Efficiently modeling long sequences with structured state spaces

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:21:29.325773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03e03214-f666-452e-ad3a-1a68133923ed · outbound

This paper cites Mamba: Linear-time sequence modeling with selective state spaces.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection Mamba: Linear-time sequence modeling with selective state spaces

Reference 52

Resolution: malformed identifier; raw_fallback, observed 2026-08-06T19:21:28.701959Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 09c25c5f-0b03-4721-ab33-7978851dbfd0 · outbound

This paper cites TimeCNN: Refining Cross-Variable Interaction on Time Point for Time Series Forecasting.

Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and Projection TimeCNN: Refining Cross-Variable Interaction on Time Point for Time Series Forecasting

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:21:28.973934Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers