Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting | Paper Citation Record

Observation 9632775f-6a9f-4037-a9de-2a0c3368d9ac · outbound

This paper cites A prediction approach for stock market volatility based on time series data.IEEE Access, 7:17287–17298.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting A prediction approach for stock market volatility based on time series data.IEEE Access, 7:17287–17298

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.493724Z

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Observation c2ab43fb-d24b-4654-8a06-e0c7aa06d555 · outbound

This paper cites Transductive LSTM for time-series prediction: An application to weather forecasting.Neural Networks, 125:1–9.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Transductive LSTM for time-series prediction: An application to weather forecasting.Neural Networks, 125:1–9

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.485769Z

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Observation dd850bcc-a5a4-4be7-8b58-2975653019b7 · outbound

This paper cites A review on time series forecasting techniques for building energy consumption.Renewable and Sustainable Energy Reviews, 74:902–924.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting A review on time series forecasting techniques for building energy consumption.Renewable and Sustainable Energy Reviews, 74:902–924

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.544758Z

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Observation 00d69458-4ce7-4f24-a808-68c8775c75f7 · outbound

This paper cites Traffic flow forecast through time series analysis based on deep learning.IEEE Access, 8:82562–82570.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Traffic flow forecast through time series analysis based on deep learning.IEEE Access, 8:82562–82570

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.496981Z

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Observation da214560-4bf4-483f-b751-871a6da3f414 · outbound

This paper cites Cross space and time: A spatio-temporal unitized model for traffic flow forecasting.IEEE Transactions on Intelligent Transportation Systems.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Cross space and time: A spatio-temporal unitized model for traffic flow forecasting.IEEE Transactions on Intelligent Transportation Systems

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.477261Z

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Observation 0748efa7-28fc-47fa-90ba-379a9a434c94 · outbound

This paper cites Predicting carpark availability in singapore with cross- domain data: a new dataset and a data-driven approach.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Predicting carpark availability in singapore with cross- domain data: a new dataset and a data-driven approach

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.538794Z

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Observation b4f80015-d45a-4f5c-b1a4-74e41c6d1b41 · outbound

This paper cites Towards multi- scenario forecasting of building electricity loads with multimodal data.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Towards multi- scenario forecasting of building electricity loads with multimodal data

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.536386Z

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Observation d0454cd7-f7ed-49ca-b5cb-c78e9f1aa97f · outbound

This paper cites Fine-grained urban heat island effect forecasting: A context-aware thermodynamic modeling framework.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Fine-grained urban heat island effect forecasting: A context-aware thermodynamic modeling framework

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.474638Z

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Observation 0527afd7-c18f-4cbd-b41d-18899b4a4861 · outbound

This paper cites Hamilton.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Hamilton

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.479927Z

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Observation 9963cb89-febb-4d75-a8cb-6325c16b4fa4 · outbound

This paper cites Anomaly and change point detection for time series with concept drift.World Wide Web, 26(5):3229–3252.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Anomaly and change point detection for time series with concept drift.World Wide Web, 26(5):3229–3252

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.482445Z

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Observation fd29ba0c-a0c0-4816-91e5-676417cbd6cc · outbound

This paper cites DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-26T02:03:08.640719Z

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Observation 519b167e-9349-4c60-847e-8c2996831298 · outbound

This paper cites Dynamic neural networks: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):7436–7456.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Dynamic neural networks: A survey.IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(11):7436–7456

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.468702Z

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Observation 18804c00-8689-4d7a-82f9-0200c7deba34 · outbound

This paper cites Dynamic convolution: Attention over convolution kernels.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Dynamic convolution: Attention over convolution kernels

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.471309Z

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Observation 1ae10f36-1f18-46f3-802f-145ae1dd316a · outbound

This paper cites Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Nguyen, Phanwadee Sinthong, and Jayant Kalagnanam

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.488690Z

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Observation 993cdd72-f22e-4510-8222-24d8b846dc21 · outbound

This paper cites iTransformer: Inverted transformers are effective for time series forecasting.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting iTransformer: Inverted transformers are effective for time series forecasting

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.491584Z

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Observation b0a41601-6d34-4622-aa76-edbc343bd87e · outbound

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

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting A decoder-only foundation model for time-series forecasting

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.509343Z

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Observation 04dd3cab-e5f2-4e7e-9e3d-992360b1bb7a · outbound

This paper cites Chronos: Learning the language of time series.Transactions on Machine Learning Research.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Chronos: Learning the language of time series.Transactions on Machine Learning Research

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.542079Z

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Observation a51620c9-d5b0-4428-8f7c-f17d635a3bdb · outbound

This paper cites Time- MoE: Billion-scale time series foundation models with mixture of experts.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Time- MoE: Billion-scale time series foundation models with mixture of experts

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.533721Z

Source-reported events for the cited work

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Observation a299ec57-a6ae-466f-8320-641ae98f7055 · outbound

This paper cites Timeexpert: Boosting long time series forecasting with temporal mix of experts.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Timeexpert: Boosting long time series forecasting with temporal mix of experts

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:01:16.655342Z

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Observation 6063cfbe-d4ef-4055-8f6e-8f2cf2e6e766 · outbound

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

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Reversible instance normalization for accurate time-series forecasting against distribution shift

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.451626Z

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Observation 9b34cfaf-a178-4163-a7f6-b3baa7b61e9b · outbound

This paper cites Attention is all you need.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Attention is all you need

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.452984Z

Source-reported events for the cited work

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Observation bfd25e02-c4cf-4839-9d58-b4c3f8e28566 · outbound

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

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Informer: Beyond efficient transformer for long sequence time-series forecasting

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.462771Z

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Observation 71c65cd7-9a5b-4fff-a20d-241cc14df64b · outbound

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

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.459874Z

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Observation f0a982f6-9e94-4214-a2b5-0fab7219b382 · outbound

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

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.531209Z

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Observation d51c85fc-5495-42d2-a05c-a2c50eb59bb6 · outbound

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

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Are Transformers Effective for Time Series Forecasting?

Reference 25

Resolution: verified exact; doi, observed 2026-05-08T21:34:13.304594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-05-19T20:22:35.554416+00:00.

Observation a548a27b-0db5-4e7f-a0a0-ddfd5b18d0dc · outbound

This paper cites TimesNet: Temporal 2d-variation modeling for general time series analysis.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting TimesNet: Temporal 2d-variation modeling for general time series analysis

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.513118Z

Source-reported events for the cited work

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Observation e0f50dc0-23bc-4e12-a7f0-c30d82e34c21 · outbound

This paper cites Timemixer: Decomposable mul- tiscale mixing for time series forecasting.arXiv preprint arXiv:2405.14616.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Timemixer: Decomposable mul- tiscale mixing for time series forecasting.arXiv preprint arXiv:2405.14616

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:01:16.667360Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation a941cf56-4d01-42e9-99d8-3e8f1c651045 · outbound

This paper cites WPMixer: Efficient multi- resolution mixing for long-term time series forecasting.Proceedings of the AAAI Conference on Artificial Intelligence, 39(18):19581–19588, April 2025.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting WPMixer: Efficient multi- resolution mixing for long-term time series forecasting.Proceedings of the AAAI Conference on Artificial Intelligence, 39(18):19581–19588, April 2025

Reference 28

Resolution: verified exact; doi, observed 2026-05-08T21:34:13.298577Z

Source-reported events for the cited work

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Observation 316571f8-98d7-44f3-aa60-5e40458c1cdb · outbound

This paper cites TimeFilter: Patch-specific spatial-temporal graph filtration for time series forecasting.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting TimeFilter: Patch-specific spatial-temporal graph filtration for time series forecasting

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.547220Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation c40fa61f-7be2-4729-804b-540377b88e85 · outbound

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

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Unified training of universal time series forecasting transformers

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.516718Z

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Observation 7995c4ef-aa87-413e-a532-c167d744344d · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.519825Z

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Observation d6428bb0-104b-4f46-ac9d-3da45c7a55b9 · outbound

This paper cites Switch Transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23 (120):1–39.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Switch Transformers: Scaling to trillion parameter models with simple and efficient sparsity.Journal of Machine Learning Research, 23 (120):1–39

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.501620Z

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Unavailable: named source frontier unavailable.

Observation 8c529a60-16b8-4f0a-b1e3-d44ca70c59c5 · outbound

This paper cites From sparse to soft mixtures of experts.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting From sparse to soft mixtures of experts

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.448701Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation a8f8e741-710f-4296-bfde-9fdc5b24ccc9 · outbound

This paper cites FiLM: Visual reasoning with a general conditioning layer.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting FiLM: Visual reasoning with a general conditioning layer

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.522227Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 73e3e4b2-aeab-4106-b9f0-aa2e46266967 · outbound

This paper cites Squeeze-and-excitation networks.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Squeeze-and-excitation networks

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.506784Z

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Observation d67282df-6119-46b8-9606-601fc352c230 · outbound

This paper cites Cbam: Convolutional block attention module.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Cbam: Convolutional block attention module

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.443440Z

Source-reported events for the cited work

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Observation 7e8da361-0349-41e2-8995-e3cb22f90f1f · outbound

This paper cites Condconv: Conditionally parameterized convolutions for efficient inference.Advances in neural information processing systems, 32.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Condconv: Conditionally parameterized convolutions for efficient inference.Advances in neural information processing systems, 32

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.446506Z

Source-reported events for the cited work

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Observation 299b9616-f16f-4599-add3-3c2a567307cc · outbound

This paper cites Non-stationary transformers: Exploring the stationarity in time series forecasting.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Non-stationary transformers: Exploring the stationarity in time series forecasting

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.455707Z

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Observation 0234d98a-03e8-4855-b84d-77d3502034d8 · outbound

This paper cites Koopa: Learning non-stationary time series dynamics with koopman predictors.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Koopa: Learning non-stationary time series dynamics with koopman predictors

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.465716Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 57fede80-82e7-44ad-8348-a31783d289a2 · outbound

This paper cites An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 40

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T19:36:06.244499Z

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Observation 3d9dc500-1a19-414d-88d0-b2d3fda53103 · outbound

This paper cites Deep residual learning for image recognition.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Deep residual learning for image recognition

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.434372Z

Source-reported events for the cited work

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Observation f8871b03-cf99-47f2-bd9d-badd7f7d78ff · outbound

This paper cites Long-Range Transformers for Dynamic Spatiotemporal Forecasting.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Long-Range Transformers for Dynamic Spatiotemporal Forecasting

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:01:16.686238Z

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Observation c6508e20-c00c-4feb-ae3d-24dcb9da978b · outbound

This paper cites Inouye, K.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Inouye, K

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.431493Z

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Observation e63adbb5-decc-44ff-8ff6-00b7b27ef8cb · outbound

This paper cites The volatility of realized volatility.Econometric Reviews, 27(1-3):46–78.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting The volatility of realized volatility.Econometric Reviews, 27(1-3):46–78

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.437515Z

Source-reported events for the cited work

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Observation 7c62b12f-b5e5-49aa-9de8-339ea75e37e6 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Adam: A Method for Stochastic Optimization

Reference 45

Resolution: verified exact; local_arxiv, observed 2026-05-11T19:01:16.700251Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation 7d7ae9bf-4082-4a10-95a6-28af102f2cc1 · outbound

This paper cites COVID19 has high V oV (1.46) from asymmetric pattern shifts: plateaus punctuated by exponential waves.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting COVID19 has high V oV (1.46) from asymmetric pattern shifts: plateaus punctuated by exponential waves

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.525637Z

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Observation 79a52162-2848-4b6d-8f0d-b12ea29d0b95 · outbound

This paper cites It has a strong daily and seasonal periodic base (rush-hour emission cycles, meteorological patterns), with episodic haze events layered on top as additive bursts.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting It has a strong daily and seasonal periodic base (rush-hour emission cycles, meteorological patterns), with episodic haze events layered on top as additive bursts

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.528325Z

Source-reported events for the cited work

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Observation 9365814a-7416-4da9-a245-fc5724b3c271 · outbound

This paper cites The high V oV reflects cross-variable heterogeneity—smooth temperature alongside bursty precipitation and wind—rather than intrinsic unpredictability per channel.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting The high V oV reflects cross-variable heterogeneity—smooth temperature alongside bursty precipitation and wind—rather than intrinsic unpredictability per channel

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.427969Z

Source-reported events for the cited work

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Observation 7367f912-dc84-420f-9bf5-9b4b2083f094 · outbound

This paper cites an unresolved cited work.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-05-26T12:47:50.440633Z

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Observation b0090565-ca1b-497e-9417-7bdeecbce2c7 · outbound

This paper cites an unresolved cited work.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-05-26T12:47:50.549535Z

Source-reported events for the cited work

Unavailable: named source frontier unavailable.

Observation ba7f83a7-02a1-401a-8395-48bee780bfcd · outbound

This paper cites Its dynamics resemble a driftless stochastic process: the log-price today is approximately the log-price yesterday plus noise.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting Its dynamics resemble a driftless stochastic process: the log-price today is approximately the log-price yesterday plus noise

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T12:47:50.424643Z

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Observation 3fd12905-3844-4352-9b93-a83bcee58945 · outbound

This paper cites steady trend.

Perceive, Route and Modulate: Dynamic Pattern Recalibration for Time Series Forecasting steady trend

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:01:16.673512Z

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