Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-23T23:03:45.096751Z
Paper Citation Record · LEDGER
Deep Time Series Models: A Comprehensive Survey and Benchmark
As of 4 August 2026, this Paper Citation Record lists 100 of 214 outbound references and 40 inbound Pith citation observations for arXiv:2407.13278.
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 2026-08-04T06:34:03.388597+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T02:44:17.977664Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-07-04T20:30:07.264791Z
Reference resolution
100 of 214 outbound references displayed
- verified exact19
- verified fuzzy71
- unresolved8
- parse uncertain0
- malformed identifier0
- metadata mismatch2
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 2f02b29b-c133-4d2e-bc23-bfe9b714cdbb · outbound
This paper cites an unresolved cited work.
Deep Time Series Models: A Comprehensive Survey and Benchmark Unresolved cited work
Reference 1
- Resolution
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Source-reported events for the cited work
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Observation d0fdb863-557d-4eec-b7e7-8707db4b1aed · outbound
This paper cites an unresolved cited work.
Deep Time Series Models: A Comprehensive Survey and Benchmark Unresolved cited work
Reference 2
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Source-reported events for the cited work
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Observation f32c3b23-2045-466f-b04b-fe2396bd7e66 · outbound
This paper cites Timesnet: Temporal 2d-variation modeling for general time series analysis.
Deep Time Series Models: A Comprehensive Survey and Benchmark Timesnet: Temporal 2d-variation modeling for general time series analysis
Reference 3
- Resolution
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Observation 3c450194-eed1-4363-b3bf-9239a919b9aa · outbound
This paper cites Trafficbert: Pre-trained model with large-scale data for long-range traffic flow forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Trafficbert: Pre-trained model with large-scale data for long-range traffic flow forecasting
Reference 4
- Resolution
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Source-reported events for the cited work
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Observation 35ce715f-2da9-413e-b246-e4c51ceb56dd · outbound
This paper cites Anomaly transformer: Time series anomaly detection with association discrepancy.
Deep Time Series Models: A Comprehensive Survey and Benchmark Anomaly transformer: Time series anomaly detection with association discrepancy
Reference 5
- Resolution
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Observation e996492c-ae97-44d5-8faf-be55668d2282 · outbound
This paper cites Interpretable weather forecasting for worldwide stations with a unified deep model.
Deep Time Series Models: A Comprehensive Survey and Benchmark Interpretable weather forecasting for worldwide stations with a unified deep model
Reference 6
- Resolution
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Source-reported events for the cited work
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Observation e689146e-9dd0-4dd8-a112-20909fafd69f · outbound
This paper cites an unresolved cited work.
Deep Time Series Models: A Comprehensive Survey and Benchmark Unresolved cited work
Reference 7
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Source-reported events for the cited work
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Observation 6a89117b-20be-408d-ba70-65ea3aa85907 · outbound
This paper cites Deep learning for time series classification: a review.
Deep Time Series Models: A Comprehensive Survey and Benchmark Deep learning for time series classification: a review
Reference 8
- Resolution
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Source-reported events for the cited work
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Observation f7cb2983-8619-42f7-b4cd-abc2fd7d8503 · outbound
This paper cites Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art.
Deep Time Series Models: A Comprehensive Survey and Benchmark Anomaly Detection in Univariate Time-series: A Survey on the State-of-the-Art
Reference 9
- Resolution
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Source-reported events for the cited work
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Observation 9ac3642c-5d35-45d0-b01d-09432ba7765b · outbound
This paper cites Deep learning for time series forecasting: a survey.
Deep Time Series Models: A Comprehensive Survey and Benchmark Deep learning for time series forecasting: a survey
Reference 10
- Resolution
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Source-reported events for the cited work
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Observation bfa14a59-a89d-4c0e-b838-c3c885a749a1 · outbound
This paper cites A re- view on outlier/anomaly detection in time series data.
Deep Time Series Models: A Comprehensive Survey and Benchmark A re- view on outlier/anomaly detection in time series data
Reference 11
- Resolution
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Source-reported events for the cited work
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Observation e0e34c04-4859-4fa3-842a-d013fda47ed3 · outbound
This paper cites Transformers in Time Series: A Survey.
Deep Time Series Models: A Comprehensive Survey and Benchmark Transformers in Time Series: A Survey
Reference 12
- Resolution
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Source-reported events for the cited work
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Observation 010bcf1d-1cfd-4efb-bdc4-964d7c5a84da · outbound
This paper cites A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection.
Deep Time Series Models: A Comprehensive Survey and Benchmark A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
Reference 13
- Resolution
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Source-reported events for the cited work
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Observation 13665458-39fd-4870-8963-367d7c24c966 · outbound
This paper cites Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis.
Deep Time Series Models: A Comprehensive Survey and Benchmark Exploring Progress in Multivariate Time Series Forecasting: Comprehensive Benchmarking and Heterogeneity Analysis
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation fd035fc9-21b6-4a7e-ab5a-685c663a640f · outbound
This paper cites TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods.
Deep Time Series Models: A Comprehensive Survey and Benchmark TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods
Reference 15
- Resolution
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Source-reported events for the cited work
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Observation b5290253-ade8-485b-afae-4275e3391ed7 · outbound
This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.
Deep Time Series Models: A Comprehensive Survey and Benchmark BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Reference 16
- Resolution
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Source-reported events for the cited work
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Observation c966f6cb-c7e7-462f-8350-a2396b691ad6 · outbound
This paper cites GPT-4 Technical Report.
Deep Time Series Models: A Comprehensive Survey and Benchmark GPT-4 Technical Report
Reference 17
- Resolution
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Source-reported events for the cited work
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Observation 551212c7-7dc1-4f3f-b234-bab0d64027ec · outbound
This paper cites Deep residual learning for image recognition.
Deep Time Series Models: A Comprehensive Survey and Benchmark Deep residual learning for image recognition
Reference 18
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4e5c7c4b-42e6-4c33-adc0-e8fe851f9bd7 · outbound
This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.
Deep Time Series Models: A Comprehensive Survey and Benchmark An image is worth 16x16 words: Transformers for image recognition at scale
Reference 19
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 2d5524a3-e853-42d2-b4f0-3391a77e614d · outbound
This paper cites Deep learning based recommender system: A survey and new perspectives.
Deep Time Series Models: A Comprehensive Survey and Benchmark Deep learning based recommender system: A survey and new perspectives
Reference 20
- Resolution
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Source-reported events for the cited work
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Observation 9fe57c8d-e9f0-4325-a214-d09439d1e2d4 · outbound
This paper cites Informer: Beyond efficient transformer for long sequence time- series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Informer: Beyond efficient transformer for long sequence time- series forecasting
Reference 21
- Resolution
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Source-reported events for the cited work
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Observation 6176bb4f-da27-4abf-9748-6477945f8719 · outbound
This paper cites Autoformer: Decomposition transformers with auto-correlation for long-term series forecast- ing.
Deep Time Series Models: A Comprehensive Survey and Benchmark Autoformer: Decomposition transformers with auto-correlation for long-term series forecast- ing
Reference 22
- Resolution
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Source-reported events for the cited work
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Observation 695a52e8-bd79-4e41-b2a4-bf19ca634778 · outbound
This paper cites A time series is worth 64 words: Long-term forecasting with transformers.
Deep Time Series Models: A Comprehensive Survey and Benchmark A time series is worth 64 words: Long-term forecasting with transformers
Reference 23
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 64ce37fb-9969-48ae-be8b-8355294a557d · outbound
This paper cites N- BEATS: neural basis expansion analysis for interpretable time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark N- BEATS: neural basis expansion analysis for interpretable time series forecasting
Reference 24
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 11ed1d0c-a143-4fd3-bb00-59ab2dabc8eb · outbound
This paper cites Some properties of time series data and their use in econometric model specification.
Deep Time Series Models: A Comprehensive Survey and Benchmark Some properties of time series data and their use in econometric model specification
Reference 25
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 57f15046-58d5-4ac4-b51f-2f271f881580 · outbound
This paper cites Co-integration and error correc- tion: representation, estimation, and testing.
Deep Time Series Models: A Comprehensive Survey and Benchmark Co-integration and error correc- tion: representation, estimation, and testing
Reference 26
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 83c5a62c-de64-4fda-bd20-409b5ba71da6 · outbound
This paper cites Vector autoregressions.
Deep Time Series Models: A Comprehensive Survey and Benchmark Vector autoregressions
Reference 27
- Resolution
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Source-reported events for the cited work
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Observation 881a2f4e-c903-425d-84bc-b5d14c8a3483 · outbound
This paper cites Spectral temporal graph neural network for multivariate time-series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Spectral temporal graph neural network for multivariate time-series forecasting
Reference 28
- Resolution
- verified fuzzy
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Source-reported events for the cited work
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Observation 611c58b6-b4f7-4357-a167-3e8bfe49be39 · outbound
This paper cites Crossformer: Transformer utilizing cross- dimension dependency for multivariate time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Crossformer: Transformer utilizing cross- dimension dependency for multivariate time series forecasting
Reference 29
- Resolution
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Source-reported events for the cited work
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Observation ce240f3f-2d54-442b-a2a5-6ceca6a466d9 · outbound
This paper cites iTransformer: Inverted Transformers Are Effective for Time Series Forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark iTransformer: Inverted Transformers Are Effective for Time Series Forecasting
Reference 30
- Resolution
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Source-reported events for the cited work
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Observation 28a5ea99-fc7f-429e-a2bf-4515e1d7fb8c · outbound
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Deep Time Series Models: A Comprehensive Survey and Benchmark Unresolved cited work
Reference 31
- Resolution
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Source-reported events for the cited work
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Observation f3356af5-6c35-462e-a4eb-8072a1ba2ee1 · outbound
This paper cites Time series forecasting using a hybrid arima and neural network model.
Deep Time Series Models: A Comprehensive Survey and Benchmark Time series forecasting using a hybrid arima and neural network model
Reference 32
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 39e9eeac-f537-4d3c-9849-a106f71d56a1 · outbound
This paper cites Time Series Data Imputation: A Survey on Deep Learning Approaches.
Deep Time Series Models: A Comprehensive Survey and Benchmark Time Series Data Imputation: A Survey on Deep Learning Approaches
Reference 33
- Resolution
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Source-reported events for the cited work
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Observation 12f446b3-c4ea-4942-a73c-420791b6a81d · outbound
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Deep Time Series Models: A Comprehensive Survey and Benchmark Multivariate time series imputation with generative adversarial networks
Reference 34
- Resolution
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Source-reported events for the cited work
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Observation 2cb54c12-e747-46c0-a23d-d699de0c2337 · outbound
This paper cites imputets: time series missing value imputation in r.
Deep Time Series Models: A Comprehensive Survey and Benchmark imputets: time series missing value imputation in r
Reference 35
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d9b9bc03-4c07-4344-b618-f6e7372df290 · outbound
This paper cites Convolutional neural networks for time series classification.
Deep Time Series Models: A Comprehensive Survey and Benchmark Convolutional neural networks for time series classification
Reference 36
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 697cff08-9500-4544-be54-1eaddd08b34f · outbound
This paper cites Generic and scalable framework for automated time-series anomaly detection.
Deep Time Series Models: A Comprehensive Survey and Benchmark Generic and scalable framework for automated time-series anomaly detection
Reference 37
- Resolution
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Source-reported events for the cited work
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Observation f32e3d9c-489f-4ab9-b7c6-654b18be5b81 · outbound
This paper cites Instance Normalization: The Missing Ingredient for Fast Stylization.
Deep Time Series Models: A Comprehensive Survey and Benchmark Instance Normalization: The Missing Ingredient for Fast Stylization
Reference 38
- Resolution
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Source-reported events for the cited work
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Observation 55bcea89-865e-4603-ba90-92d5e82039e6 · outbound
This paper cites Deep adaptive input normalization for time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Deep adaptive input normalization for time series forecasting
Reference 39
- Resolution
- verified fuzzy
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ada79782-5e06-4346-8943-1f246ec215bb · outbound
This paper cites Reversible instance normalization for accurate time-series forecasting against distribution shift.
Deep Time Series Models: A Comprehensive Survey and Benchmark Reversible instance normalization for accurate time-series forecasting against distribution shift
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:36.833022Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1a179eee-8193-4edb-8d74-163ca78504a4 · outbound
This paper cites Non-stationary trans- formers: Exploring the stationarity in time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Non-stationary trans- formers: Exploring the stationarity in time series forecasting
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:36.957914Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ecdfb6d3-1770-414d-bdd6-8d37b240f949 · outbound
This paper cites Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Client: Cross-variable Linear Integrated Enhanced Transformer for Multivariate Long-Term Time Series Forecasting
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T23:05:51.476581Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4d30a90c-4858-4854-a3a9-3c84c4b43f6b · outbound
This paper cites CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark CSformer: Combining Channel Independence and Mixing for Robust Multivariate Time Series Forecasting
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T23:05:51.406005Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6a9c87a2-352f-4253-82dc-393cfb5053a5 · outbound
This paper cites Adaptive normalization for non-stationary time series forecasting: A temporal slice perspective.
Deep Time Series Models: A Comprehensive Survey and Benchmark Adaptive normalization for non-stationary time series forecasting: A temporal slice perspective
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.060405Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c034bb00-1eb7-4680-9433-88a56ceb02d6 · outbound
This paper cites Stl: A seasonal-trend decomposition.
Deep Time Series Models: A Comprehensive Survey and Benchmark Stl: A seasonal-trend decomposition
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:36.836780Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f0c6e360-aa8b-455f-be07-22e9ab667b6f · outbound
This paper cites Time-series. 2nd edn.
Deep Time Series Models: A Comprehensive Survey and Benchmark Time-series. 2nd edn
Reference 46
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.043180Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b7dd2044-bb12-4314-a5d9-e7e749f6d7d0 · outbound
This paper cites Stl: A seasonal-trend decomposition procedure based on loess.
Deep Time Series Models: A Comprehensive Survey and Benchmark Stl: A seasonal-trend decomposition procedure based on loess
Reference 47
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.046686Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5df80772-ed8f-4729-8fff-a3b9ddc99956 · outbound
This paper cites an unresolved cited work.
Deep Time Series Models: A Comprehensive Survey and Benchmark Unresolved cited work
Reference 48
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T23:28:37.049889Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 674b8a1b-c231-40e0-a749-639fe25e3760 · outbound
This paper cites Robuststl: A robust seasonal-trend decomposition algorithm for long time series.
Deep Time Series Models: A Comprehensive Survey and Benchmark Robuststl: A robust seasonal-trend decomposition algorithm for long time series
Reference 49
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.057146Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b87b4666-e50f-4ec9-a23b-e2b5189df18d · outbound
This paper cites Forecasting time series with complex seasonal patterns using exponential smoothing.
Deep Time Series Models: A Comprehensive Survey and Benchmark Forecasting time series with complex seasonal patterns using exponential smoothing
Reference 50
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.081882Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d0665b0b-4614-46b4-9a16-1c3b2d070494 · outbound
This paper cites Forecasting at scale.
Deep Time Series Models: A Comprehensive Survey and Benchmark Forecasting at scale
Reference 51
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.088543Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 62607cf1-65e6-4f71-a873-5b3de3a0e1c8 · outbound
This paper cites Are transformers effective for time series forecasting?.
Deep Time Series Models: A Comprehensive Survey and Benchmark Are transformers effective for time series forecasting?
Reference 52
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.040037Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4ca1e3fa-e80c-4c58-ae9b-75992eb85edc · outbound
This paper cites Inpar- former: evolutionary decomposition transformers with interactive parallel attention for long-term time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Inpar- former: evolutionary decomposition transformers with interactive parallel attention for long-term time series forecasting
Reference 53
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:36.965933Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6efabb86-ae7f-46b7-b22d-f2cc89948455 · outbound
This paper cites Preformer: predictive transformer with multi-scale segment-wise correlations for long-term time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Preformer: predictive transformer with multi-scale segment-wise correlations for long-term time series forecasting
Reference 54
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.025048Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f37eb6df-cda2-4e00-911f-bceea0b3811c · outbound
This paper cites TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting
Reference 55
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-23T23:05:51.467777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 14941d6b-006a-4121-b201-f885ff13c11a · outbound
This paper cites N-BEATS: Neural basis expansion analysis for interpretable time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark N-BEATS: Neural basis expansion analysis for interpretable time series forecasting
Reference 56
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T23:05:51.375660Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 156317d8-36fd-49f4-b362-194bde448769 · outbound
This paper cites Nhits: Neural hierarchical interpo- lation for time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Nhits: Neural hierarchical interpo- lation for time series forecasting
Reference 57
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.030177Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8c0366b2-a321-4231-a026-62d5858df32b · outbound
This paper cites Depts: Deep expansion learning for periodic time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Depts: Deep expansion learning for periodic time series forecasting
Reference 58
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.033364Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c81088a2-0691-4d39-b484-73b51ce14624 · outbound
This paper cites Dewp: Deep expansion learning for wind power forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Dewp: Deep expansion learning for wind power forecasting
Reference 59
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:36.969655Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation fed7fc78-1971-407d-8e9b-fc38b7de331c · outbound
This paper cites Temporal collaborative filtering with bayesian probabilistic tensor factorization.
Deep Time Series Models: A Comprehensive Survey and Benchmark Temporal collaborative filtering with bayesian probabilistic tensor factorization
Reference 60
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.075202Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 40ca7985-5a5e-47c7-8791-7c9e1b18ee64 · outbound
This paper cites Temporal regularized matrix factorization for high-dimensional time series prediction.
Deep Time Series Models: A Comprehensive Survey and Benchmark Temporal regularized matrix factorization for high-dimensional time series prediction
Reference 61
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:54.235063Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation febbb926-488d-4746-9865-d496d562c8f1 · outbound
This paper cites Autoregressive tensor factorization for spatio-temporal predictions.
Deep Time Series Models: A Comprehensive Survey and Benchmark Autoregressive tensor factorization for spatio-temporal predictions
Reference 62
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.120578Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 73275c70-6fd0-4c3c-aad8-ae8dd75f2147 · outbound
This paper cites Nonsta- tionary temporal matrix factorization for multivariate time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Nonsta- tionary temporal matrix factorization for multivariate time series forecasting
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T23:05:51.530647Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3da1d47a-dbeb-41ce-87f1-5bec6ca7363e · outbound
This paper cites Bayesian temporal factorization for multi- dimensional time series prediction.
Deep Time Series Models: A Comprehensive Survey and Benchmark Bayesian temporal factorization for multi- dimensional time series prediction
Reference 64
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:54.113959Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d76baf81-1d45-48cb-ae25-fb9b3efa1565 · outbound
This paper cites Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Think globally, act locally: A deep neural network approach to high-dimensional time series forecasting
Reference 65
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:54.125535Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 03bf5a7c-3fee-4860-8782-43b18d090e19 · outbound
This paper cites Real-time spatiotemporal prediction and imputation of traffic status based on lstm and graph laplacian regularized matrix factorization.
Deep Time Series Models: A Comprehensive Survey and Benchmark Real-time spatiotemporal prediction and imputation of traffic status based on lstm and graph laplacian regularized matrix factorization
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:54.145621Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 13a906b2-5c5d-4054-8e65-53a8205b9728 · outbound
This paper cites Graph regularized nonnegative matrix factorization for data representation.
Deep Time Series Models: A Comprehensive Survey and Benchmark Graph regularized nonnegative matrix factorization for data representation
Reference 67
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:36.843435Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3acb8c22-b20f-44c2-812a-607b505388d9 · outbound
This paper cites an unresolved cited work.
Deep Time Series Models: A Comprehensive Survey and Benchmark Unresolved cited work
Reference 68
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T23:28:36.840014Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation e191bc55-b26c-4901-aa48-90b74ecb7e62 · outbound
This paper cites an unresolved cited work.
Deep Time Series Models: A Comprehensive Survey and Benchmark Unresolved cited work
Reference 69
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T23:28:36.979795Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b6a22ba6-42f4-4929-aca2-23a9489c06e0 · outbound
This paper cites Bloomfield,Fourier analysis of time series: an introduction.
Deep Time Series Models: A Comprehensive Survey and Benchmark Bloomfield,Fourier analysis of time series: an introduction
Reference 70
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:54.086308Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5439735d-0dd7-4c97-be65-7e0d2f87628e · outbound
This paper cites Epileptic seizure classifi- cation of eeg time-series using rational discrete short-time fourier transform.
Deep Time Series Models: A Comprehensive Survey and Benchmark Epileptic seizure classifi- cation of eeg time-series using rational discrete short-time fourier transform
Reference 71
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:54.090248Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9f0b6f45-b6ea-493a-9409-12b09e61a3a7 · outbound
This paper cites an unresolved cited work.
Deep Time Series Models: A Comprehensive Survey and Benchmark Unresolved cited work
Reference 72
- Resolution
- unresolved
- raw_fallback, observed 2026-05-23T23:28:36.982876Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 215ff1a7-0b4f-4cd8-9110-7f30e06b1a25 · outbound
This paper cites Meyer, Wavelets and operators: volume 1.
Deep Time Series Models: A Comprehensive Survey and Benchmark Meyer, Wavelets and operators: volume 1
Reference 73
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.036599Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3c9491f9-b212-446b-a980-42ab43a2fb7a · outbound
This paper cites Multilevel wavelet decompo- sition network for interpretable time series analysis.
Deep Time Series Models: A Comprehensive Survey and Benchmark Multilevel wavelet decompo- sition network for interpretable time series analysis
Reference 74
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:36.873983Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 02e4bfd1-369b-4046-9ffc-b04589bc51b8 · outbound
This paper cites Stfnets: Learning sensing signals from the time-frequency perspective with short-time fourier neural networks.
Deep Time Series Models: A Comprehensive Survey and Benchmark Stfnets: Learning sensing signals from the time-frequency perspective with short-time fourier neural networks
Reference 75
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:28:37.009752Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f992c178-4035-4957-9434-bb16dd5ad4c1 · outbound
This paper cites Wavelet transform application for/in non-stationary time-series analysis: A review.
Deep Time Series Models: A Comprehensive Survey and Benchmark Wavelet transform application for/in non-stationary time-series analysis: A review
Reference 76
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.971420Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1070b880-403d-4040-9fd4-24306079ebed · outbound
This paper cites T-wavenet: A tree-structured wavelet neural network for time series signal analysis.
Deep Time Series Models: A Comprehensive Survey and Benchmark T-wavenet: A tree-structured wavelet neural network for time series signal analysis
Reference 77
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.953936Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f7c65e8f-a538-4490-a18b-ddf3312b1a59 · outbound
This paper cites Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion.
Deep Time Series Models: A Comprehensive Survey and Benchmark Unsupervised time-series representation learning with iterative bilinear temporal-spectral fusion
Reference 78
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.947045Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation de3b5ac8-e92f-4982-8b1c-03a424a02714 · outbound
This paper cites When: A wavelet-dtw hybrid attention network for heterogeneous time series analysis.
Deep Time Series Models: A Comprehensive Survey and Benchmark When: A wavelet-dtw hybrid attention network for heterogeneous time series analysis
Reference 79
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.950295Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8b9b69b0-392b-4e4d-8cba-3c362d95dc24 · outbound
This paper cites Waveform: Graph enhanced wavelet learning for long sequence forecasting of multivariate time series.
Deep Time Series Models: A Comprehensive Survey and Benchmark Waveform: Graph enhanced wavelet learning for long sequence forecasting of multivariate time series
Reference 80
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.957256Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9f6d174a-b84b-4618-8002-d5db43042141 · outbound
This paper cites A Survey on Deep Learning based Time Series Analysis with Frequency Transformation.
Deep Time Series Models: A Comprehensive Survey and Benchmark A Survey on Deep Learning based Time Series Analysis with Frequency Transformation
Reference 81
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T23:05:51.521362Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1c90631e-2a44-459b-9166-dc387d29151b · outbound
This paper cites Period- icity decoupling framework for long-term series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Period- icity decoupling framework for long-term series forecasting
Reference 82
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.967882Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 9a2aa4a4-9e56-41f9-bbf5-11b2466df7e6 · outbound
This paper cites Generalized harmonic analysis.
Deep Time Series Models: A Comprehensive Survey and Benchmark Generalized harmonic analysis
Reference 83
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.923115Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5677dd17-fb0d-46ae-818f-6e7ee73ef4b8 · outbound
This paper cites Film: Frequency improved legendre memory model for long-term time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Film: Frequency improved legendre memory model for long-term time series forecasting
Reference 84
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:05:52.759956Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation fbbe2ab5-81a1-47bd-be8d-8d93f57d84e8 · outbound
This paper cites FITS: Modeling Time Series with $10k$ Parameters.
Deep Time Series Models: A Comprehensive Survey and Benchmark FITS: Modeling Time Series with $10k$ Parameters
Reference 85
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T23:05:51.473947Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 679162de-5f56-4c9f-88c7-2a833455b58d · outbound
This paper cites Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Fedformer: Frequency enhanced decomposed transformer for long-term series forecasting
Reference 86
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.919399Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 816ebfac-c1ae-4c09-88bc-c4ec4fa770e5 · outbound
This paper cites Adaptive temporal- frequency network for time-series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Adaptive temporal- frequency network for time-series forecasting
Reference 87
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.927113Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 67d611a0-61fa-42eb-92b1-f53d5182b487 · outbound
This paper cites Tfad: A decomposition time series anomaly detection architecture with time-frequency analysis.
Deep Time Series Models: A Comprehensive Survey and Benchmark Tfad: A decomposition time series anomaly detection architecture with time-frequency analysis
Reference 88
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:05:52.750462Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 968c3cce-e240-4110-a20d-56f75e4ac0ef · outbound
This paper cites Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Edge-Varying Fourier Graph Networks for Multivariate Time Series Forecasting
Reference 89
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T23:05:51.493326Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation c0cbee48-8e2b-4830-93a1-29276703f283 · outbound
This paper cites Frequency-domain mlps are more effective learners in time series forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark Frequency-domain mlps are more effective learners in time series forecasting
Reference 90
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.931637Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 98de9713-8e6b-4c81-81b0-98fe2235310b · outbound
This paper cites Revisiting vae for unsupervised time series anomaly detection: A frequency perspective.
Deep Time Series Models: A Comprehensive Survey and Benchmark Revisiting vae for unsupervised time series anomaly detection: A frequency perspective
Reference 91
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:05:52.746546Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b60788ee-4974-48fe-827b-2ca8fc91baa8 · outbound
This paper cites Tslanet: Rethinking transformers for time series representation learning.
Deep Time Series Models: A Comprehensive Survey and Benchmark Tslanet: Rethinking transformers for time series representation learning
Reference 92
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:05:52.755262Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation dc2ae34d-c72e-4b4d-b0f6-4b7cb97f6750 · outbound
This paper cites Time series diffusion in the frequency domain.
Deep Time Series Models: A Comprehensive Survey and Benchmark Time series diffusion in the frequency domain
Reference 93
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:05:52.743690Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 92ee47f6-535b-4348-9ec0-6c15f14c5e07 · outbound
This paper cites Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with nbeatsx.
Deep Time Series Models: A Comprehensive Survey and Benchmark Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with nbeatsx
Reference 94
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.907929Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d68d1c29-bad1-4cc6-bd5f-ef5228ce4b6f · outbound
This paper cites Mlp-mixer: An all-mlp architecture for vision.
Deep Time Series Models: A Comprehensive Survey and Benchmark Mlp-mixer: An all-mlp architecture for vision
Reference 95
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.915700Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5593e975-0c74-4566-9b85-17362cacb14d · outbound
This paper cites TSMixer: An All-MLP Architecture for Time Series Forecasting.
Deep Time Series Models: A Comprehensive Survey and Benchmark TSMixer: An All-MLP Architecture for Time Series Forecasting
Reference 96
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-23T23:05:51.489070Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8560e9d9-1c34-4499-80d9-f25c199405ee · outbound
This paper cites Long-term Forecasting with TiDE: Time-series Dense Encoder.
Deep Time Series Models: A Comprehensive Survey and Benchmark Long-term Forecasting with TiDE: Time-series Dense Encoder
Reference 97
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T23:05:51.552811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 18c2655c-8601-4267-a17d-5005a60e1b7e · outbound
This paper cites Dynamic mode decomposition of numerical and experimental data.
Deep Time Series Models: A Comprehensive Survey and Benchmark Dynamic mode decomposition of numerical and experimental data
Reference 98
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.895260Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 6d269ab3-eb8d-40da-a592-1ea508335c2a · outbound
This paper cites Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors.
Deep Time Series Models: A Comprehensive Survey and Benchmark Koopa: Learning Non-stationary Time Series Dynamics with Koopman Predictors
Reference 99
- Resolution
- verified exact
- arxiv_id, observed 2026-05-23T23:05:51.506357Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0d361010-09e1-45bd-937c-1ba96dfe1916 · outbound
This paper cites Long short-term memory.
Deep Time Series Models: A Comprehensive Survey and Benchmark Long short-term memory
Reference 100
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-23T23:25:55.899502Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Pith citing papers
Observation 22870793-f4c9-4eb8-bd7a-352545ac10af · inbound
Universal Time-Series Representation Learning: A Survey cites this paper.
Universal Time-Series Representation Learning: A Survey Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 202
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T04:28:53.222429Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation caa9d7c7-5687-4df3-883c-e9d28b544e26 · inbound
FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting cites this paper.
FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 70
- Resolution
- verified exact
- local_arxiv, observed 2026-05-23T02:52:26.574984Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1ade03a6-8610-4b5f-8b80-058e9b618b90 · inbound
MSDformer: Multi-scale Discrete Transformer For Time Series Generation cites this paper.
MSDformer: Multi-scale Discrete Transformer For Time Series Generation Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 48
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T13:44:52.835558Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 24ba955c-b4b5-4b0d-a551-22db322294fc · inbound
DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment cites this paper.
DistDF: Time-Series Forecasting Needs Joint-Distribution Wasserstein Alignment Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 3
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-05-18T03:00:48.613103Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 3512606c-2015-4628-a328-7abb850fb338 · inbound
AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecasting cites this paper.
AlphaCast: A Human Wisdom-LLM Intelligence Co-Reasoning Framework for Interactive Time Series Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 20
- Resolution
- verified exact
- local_arxiv, observed 2026-05-17T23:10:26.090854Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 5650c6b5-6908-4ca5-9198-cf5250562f6b · inbound
Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring cites this paper.
Delta-XAI: A Unified Framework for Explaining Prediction Changes in Online Time Series Monitoring Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-05-17T04:11:30.700327Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d661dfa7-3ece-4509-879a-97476e62966a · inbound
Neural CDEs as Correctors for Learned Time Series Models cites this paper.
Neural CDEs as Correctors for Learned Time Series Models Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 21
- Resolution
- verified exact
- local_arxiv, observed 2026-05-16T23:28:41.072050Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation cc622e76-995a-433c-82db-ed706e0038a8 · inbound
DeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysis cites this paper.
DeMa: Dual-Path Delay-Aware Mamba for Efficient Multivariate Time Series Analysis Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 33
- Resolution
- verified exact
- local_arxiv, observed 2026-05-21T16:24:15.910572Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 68df422d-69bc-469f-9fd5-ad013def0493 · inbound
Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting cites this paper.
Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 45
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T02:44:17.977664Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a9b3fad4-ccab-4336-8a9f-2a79f6c8cbba · 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 Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 53
- Resolution
- verified exact
- local_arxiv, observed 2026-05-15T16:50:10.918634Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 75d888c4-c5f2-4d81-b60b-980f2372a920 · inbound
MambaSL: Exploring Single-Layer Mamba for Time Series Classification cites this paper.
MambaSL: Exploring Single-Layer Mamba for Time Series Classification Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 15
- Resolution
- verified exact
- local_arxiv, observed 2026-05-10T11:30:18.664922Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 4582596d-d96a-45dc-9035-15fd874c16bb · inbound
Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring cites this paper.
Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 24
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T13:36:09.889708Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 7b93dc0b-89e5-41d8-9b70-0479bdfee46a · inbound
CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting cites this paper.
CastFlow: Learning Role-Specialized Agentic Workflows for Time Series Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T10:31:30.352626Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 634baca6-9cfe-482b-af9a-e3061914db2b · inbound
TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning cites this paper.
TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 72
- Resolution
- verified exact
- local_arxiv, observed 2026-05-10T07:16:54.860560Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 81ef794f-68d6-4a1f-ab4a-d0370031b20e · inbound
TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning cites this paper.
TimeRFT: Stimulating Generalizable Time Series Forecasting for TSFMs via Reinforcement Finetuning Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 67
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T02:26:14.792726Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4513bc6e-c319-47fc-b8ea-070513849073 · inbound
Partial Effective Information Decomposition for Synergistic Causality cites this paper.
Partial Effective Information Decomposition for Synergistic Causality Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 35
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T08:51:24.721205Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 410815a9-7333-437b-80cf-22036aa9be3a · inbound
TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting cites this paper.
TSNN: A Non-parametric and Interpretable Framework for Traffic Time Series Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T02:06:15.244096Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8a672c05-3caf-460e-9f6f-7f3941a3e5cf · inbound
LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling cites this paper.
LeapTS: Rethinking Time Series Forecasting as Adaptive Multi-Horizon Scheduling Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 131
- Resolution
- verified exact
- local_arxiv, observed 2026-05-12T06:06:26.985069Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 308e1eeb-e14d-4c27-9048-864666225275 · inbound
Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density cites this paper.
Olivia: Harmonizing Time Series Foundation Models with Power Spectral Density Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 17
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T14:43:22.427698Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f5f63725-b6c8-47a9-bb22-339d2841e1e8 · inbound
GenTS: A Comprehensive Benchmark Library for Generative Time Series Models cites this paper.
GenTS: A Comprehensive Benchmark Library for Generative Time Series Models Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 52
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T13:18:18.414711Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 02eb08de-03de-4801-bb81-c89bc22991b5 · inbound
Quantifying the Pre-training Dividend: Generative versus Latent Self-Supervised Learning for Time Series Foundation Models cites this paper.
Quantifying the Pre-training Dividend: Generative versus Latent Self-Supervised Learning for Time Series Foundation Models Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 18
- Resolution
- verified exact
- local_arxiv, observed 2026-05-20T07:13:06.538935Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 8a6c376f-a75d-43ce-bfa7-4080f4b77987 · inbound
PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting cites this paper.
PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T00:30:49.105554Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 06d9696c-e386-41a4-ac83-13637a027ba9 · inbound
CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification cites this paper.
CASE-NET: Deep Spatio-Temporal Representation Learning via Causal Attention and Channel Recalibration for Multivariate Time Series Classification Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 28
- Resolution
- verified exact
- local_arxiv, observed 2026-05-22T08:31:16.792579Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 0077acf9-7c9b-4bb4-831f-67687741e1ee · inbound
Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting cites this paper.
Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-06-30T12:34:39.212082Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 09000122-82cb-49a2-8b9d-be3b29f3cfa0 · inbound
Picid: A Modular Evaluation Infrastructure for Reproducible PHM Across Tasks and Domains cites this paper.
Picid: A Modular Evaluation Infrastructure for Reproducible PHM Across Tasks and Domains Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 5
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T12:43:25.571811Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 49ba33d3-d43f-47ef-838c-b045d8d7d783 · inbound
Why Do Time Series Models Need Long Context Windows? cites this paper.
Why Do Time Series Models Need Long Context Windows? Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 3
- Resolution
- verified exact
- local_arxiv, observed 2026-07-01T21:56:16.555808Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d21981dc-e2d6-4f15-ab62-c36c3dc3c27c · inbound
Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty cites this paper.
Expectations vs. Realities: The Cost of MSE-Optimal Forecasting Under Conditional Uncertainty Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 31
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T07:06:44.391165Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ad6e2289-502c-4538-b4ea-ef31e7f7eb01 · inbound
Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors cites this paper.
Trio: Learning Time-Series Forecasting with Temporal-Spatial-Sample Attention and Structural Causal Priors Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 27
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-02T16:27:09.663441Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation ce8292b2-3050-4a00-b4e4-c35ba72f8113 · inbound
GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks cites this paper.
GeoGNN: Time Series Geo-Localization using Two-Tower Graph Neural Networks Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 56
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T20:47:22.970107Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation b377d431-6759-414e-8417-9ece8d905322 · inbound
InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs cites this paper.
InA-Probe: Instruction-Aware Active Probing for Time Series Forecasting with LLMs Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 12
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T22:27:26.601383Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation f7fd8d66-67fa-4c3b-8b0c-b1d11b66dbee · inbound
Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection cites this paper.
Disjoint or Overlapping? Inference Windowing for Reconstruction-Based Time Series Anomaly Detection Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 10
- Resolution
- verified exact
- local_arxiv, observed 2026-07-02T02:26:27.383204Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 19b37b13-54ae-4cd0-bee6-f97850121abf · inbound
SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering cites this paper.
SCAN: Enhance Time Series Anomaly Detection via Multi-Scale Neighborhood-Centered Clustering Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 8
- Resolution
- verified exact
- local_arxiv, observed 2026-07-04T00:39:16.732239Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 1062e127-12a5-4f01-a2fc-fa444fabce94 · inbound
SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting cites this paper.
SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 57
- Resolution
- verified exact
- local_arxiv, observed 2026-07-04T05:59:38.407013Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation d5ed134f-f7d6-484c-b2da-a416a69fe330 · inbound
SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting cites this paper.
SqLinear: Balanced Square Partitioning Makes Linear Interaction Sufficient for Large-Scale Traffic Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 57
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T02:09:41.337387Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5b4b90a1-2bd7-435d-ba20-f3932f98218f · inbound
$\text{DT}^2$: Decision-Targeted Digital Twins cites this paper.
$\text{DT}^2$: Decision-Targeted Digital Twins Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 33
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-04T20:30:07.266172Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 40ec4b7e-f2fc-48e4-a8e1-fb72b00fb122 · inbound
PMDformer: Patch-Mean Decoupling Information Transformer for Long-term Forecasting cites this paper.
PMDformer: Patch-Mean Decoupling Information Transformer for Long-term Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 13
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-04T13:19:50.482307Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 84f4ec89-4dab-476f-9366-bb8d0d16ad77 · inbound
Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins cites this paper.
Timesynth: A Temporal Fidelity Framework for Health Signal Digital Twins Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 10
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-07-02T16:37:09.014696Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation dc701efa-59b7-4362-b07d-0bb8af46c9a4 · inbound
Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis cites this paper.
Zeus: Towards Tuning-Free Foundation Model for Time Series Analysis Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 147
- Resolution
- verified exact
- local_arxiv, observed 2026-07-03T17:38:43.294696Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-04T06:34:03.388597+00:00.
Observation 35b8fb1d-fe34-4cb5-ad17-38c57cb4d9a0 · inbound
Modular Foundation Models for Time-Series Perception in Digital Twins cites this paper.
Modular Foundation Models for Time-Series Perception in Digital Twins Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 114
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T01:22:51.284207Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c7bdb255-cd54-40d4-b47d-63bc26861563 · inbound
CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting cites this paper.
CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T07:50:39.484871Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.