Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics | Paper Citation Record

Observation 0508f415-ec4f-4270-aa71-ccb1cda4bd74 · outbound

This paper cites A machine learning model that outperforms conventional global subseasonal forecast models,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics A machine learning model that outperforms conventional global subseasonal forecast models,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:42.731742Z

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Observation 7c345074-a2f6-45f2-a727-39cb1b0f8e71 · outbound

This paper cites Forecasting Andean rainfall and crop yield from the influence of El Niñ o on Pleiades visibility,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Forecasting Andean rainfall and crop yield from the influence of El Niñ o on Pleiades visibility,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:42.586456Z

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Observation ea959e87-e6ed-45a5-917e-4f36be63fb63 · outbound

This paper cites Self -organizing maps of typhoon tracks allow for flood forecasts up to two days in advance,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Self -organizing maps of typhoon tracks allow for flood forecasts up to two days in advance,

Reference 3

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This paper cites Stock price prediction using LSTM, RNN and CNN -sliding window model,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Stock price prediction using LSTM, RNN and CNN -sliding window model,

Reference 4

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This paper cites Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey,

Reference 5

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Observation c00aa5ea-763a-49da-8351-dfea46cf1b6d · outbound

This paper cites Baroreflex sensitivity and heart -rate variability in prediction of total cardiac mortality after myocardial infarction,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Baroreflex sensitivity and heart -rate variability in prediction of total cardiac mortality after myocardial infarction,

Reference 6

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Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Unresolved cited work

Reference 7

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Observation 191b5a1a-ce67-4a0f-add1-3bd2e9a770c2 · outbound

This paper cites Machine learning based early warning system enables accurate mortality risk prediction for COVID-19,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Machine learning based early warning system enables accurate mortality risk prediction for COVID-19,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:41.703537Z

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Observation d9f54043-780b-4f60-a2e2-b26fcc1d478b · outbound

This paper cites Machine learning-based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Machine learning-based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals,

Reference 9

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This paper cites On-line building energy optimization using deep reinforcement learning,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics On-line building energy optimization using deep reinforcement learning,

Reference 10

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Observation f870ef82-c07e-4149-bc75-239435936947 · outbound

This paper cites A review on time series data mining,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics A review on time series data mining,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:41.031974Z

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Observation a58a4b09-4e55-44d1-b103-3245c71d4f76 · outbound

This paper cites Deep learning for time series classification: a review,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Deep learning for time series classification: a review,

Reference 12

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Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Unresolved cited work

Reference 13

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This paper cites Nearly efficient estimation of time series models with predetermined, but not exogenous, instruments,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Nearly efficient estimation of time series models with predetermined, but not exogenous, instruments,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:40.659581Z

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This paper cites Vector autoregressive models,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Vector autoregressive models,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:40.469033Z

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This paper cites A training algorithm for optimal margin classifiers,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics A training algorithm for optimal margin classifiers,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:40.345339Z

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Observation 33bb42c3-c750-4d6e-a9e9-a28cc3826713 · outbound

This paper cites Natural language processing,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Natural language processing,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:40.184126Z

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Observation 86fbd771-8805-4c46-8a6d-0ac995922b0b · outbound

This paper cites Deep residual learning for image recognition,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Deep residual learning for image recognition,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:40.037505Z

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Observation 8ee93e52-6986-49b4-be51-3b517cc114c8 · outbound

This paper cites Recurrent neural networks and robust time series prediction,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Recurrent neural networks and robust time series prediction,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:39.889207Z

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Observation 133cbb60-d832-4014-ae45-2ad3de02c9f4 · outbound

This paper cites Stock price pattern recognition-a recurrent neural network approach,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Stock price pattern recognition-a recurrent neural network approach,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:39.762202Z

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Observation 4e42ffee-636b-47f8-bfaa-c99dc864bd1d · outbound

This paper cites Temporal convolutional networks for action segmentation and detection,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Temporal convolutional networks for action segmentation and detection,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:39.624501Z

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This paper cites Are transformers effective for time series forecasting?,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Are transformers effective for time series forecasting?,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:39.467262Z

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Observation 814d31c1-89ec-4534-9cd5-694d7f4278aa · outbound

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

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Informer: Beyond efficient transformer for long sequence time-series forecasting,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:39.327355Z

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Observation 7944b33b-3efc-4a2a-a9e3-7a46fb0da9cb · outbound

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

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Revisiting Long-term Time Series Forecasting: An Investigation on Linear Mapping

Reference 24

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

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T04:09:31.356578Z

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

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:39.167850Z

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

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics iTransformer: Inverted Transformers Are Effective for Time Series Forecasting,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:39.007272Z

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Observation cc603180-66f2-4335-bacb-edee8e11de87 · outbound

This paper cites From Similarity to Superiority: Channel Clustering for Time Series Forecasting.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics From Similarity to Superiority: Channel Clustering for Time Series Forecasting

Reference 28

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This paper cites The capacity and robustness trade-off: Revisiting the channel independent strategy for multivariate time series forecasting,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics The capacity and robustness trade-off: Revisiting the channel independent strategy for multivariate time series forecasting,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:38.855856Z

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This paper cites Foundation models for time series analysis: A tutorial and survey,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Foundation models for time series analysis: A tutorial and survey,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:38.711586Z

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This paper cites Complex network from time series based on phase space reconstruction,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Complex network from time series based on phase space reconstruction,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:38.556721Z

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This paper cites Determining Lyapunov exponents from a time series,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Determining Lyapunov exponents from a time series,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:38.379212Z

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Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Ergodic theory of chaos and strange attractors,

Reference 33

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Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Unresolved cited work

Reference 34

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This paper cites Latent ordinary differential equations for irregularly -sampled time series,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Latent ordinary differential equations for irregularly -sampled time series,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:37.579650Z

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This paper cites Detecting strange attractors in turbulence,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Detecting strange attractors in turbulence,

Reference 36

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This paper cites Nonlinear dynamics, delay times, and embedding windows,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Nonlinear dynamics, delay times, and embedding windows,

Reference 37

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Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics The dimension of chaotic attractors,

Reference 38

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This paper cites Randomly distributed embedding making short -term high-dimensional data predictable,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Randomly distributed embedding making short -term high-dimensional data predictable,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:36.609566Z

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This paper cites Predicting future dynamics from short - term time series using an Anticipated Learning Machine,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Predicting future dynamics from short - term time series using an Anticipated Learning Machine,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:36.415111Z

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This paper cites Autoreservoir computing for multistep ahead prediction based on the spatiotemporal information transformation,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Autoreservoir computing for multistep ahead prediction based on the spatiotemporal information transformation,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:36.199087Z

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Observation c7f93163-abcf-4e1c-988e-177e2f6c317d · outbound

This paper cites Spatiotemporal Transformer Neural Network for Time - Series Forecasting,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Spatiotemporal Transformer Neural Network for Time - Series Forecasting,

Reference 42

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This paper cites Predicting time series by data -driven spatiotemporal information transformation,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Predicting time series by data -driven spatiotemporal information transformation,

Reference 43

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This paper cites Spatiotemporal information conversion machine for time -series forecasting,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Spatiotemporal information conversion machine for time -series forecasting,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:35.786812Z

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This paper cites Predicting multiple observations in complex systems through low -dimensional embeddings,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Predicting multiple observations in complex systems through low -dimensional embeddings,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:35.591906Z

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Observation 29934ca9-7659-4009-b099-da83c6d69ea8 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 46

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

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis

Reference 47

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

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Autoformer: Decomposition transformers with auto -correlation for long-term series forecasting,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:35.374553Z

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Observation 6c593d5c-06d5-491d-b580-cf1702a8569c · outbound

This paper cites Modeling long-and short -term temporal patterns with deep neural networks,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Modeling long-and short -term temporal patterns with deep neural networks,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:35.162612Z

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Observation 8a4934ca-efbe-4e92-b16d-c1fc7e181027 · outbound

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

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Fedformer: Frequency enhanced decomposed transformer for long -term series forecasting,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:34.980804Z

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Observation c049c592-4e91-4866-b8d4-098ca1ed5358 · outbound

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

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Non -stationary transformers: Exploring the stationarity in time series forecasting,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:09:34.801104Z

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Observation 1d037982-ddf1-4d16-872b-af6e131a4f5b · outbound

This paper cites Long-term Forecasting with TiDE: Time-series Dense Encoder.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Long-term Forecasting with TiDE: Time-series Dense Encoder

Reference 52

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

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Lag-Llama: Towards Foundation Models for Probabilistic Time Series Forecasting

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Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics TimeGPT-1

Reference 54

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This paper cites Timer: Generative Pre -trained Transformers Are Large Time Series Models,.

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Timer: Generative Pre -trained Transformers Are Large Time Series Models,

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Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Hamiltonian Systems and Transformation in Hilbert Space,

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Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Predicting Time Series from Short -Term High -Dimensional Data,

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Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics Cvt: Introducing convolutions to vision transformers,

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

Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics A decoder-only foundation model for time-series forecasting

Reference 59

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Delayformer: spatiotemporal transformation for predicting high-dimensional dynamics MOMENT: A Family of Open Time-series Foundation Models

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