Paper Citation Record · LEDGER

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting

As of 17 August 2026, this Paper Citation Record lists 36 of 36 outbound references and 0 inbound Pith citation observations for arXiv:2411.17382.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.17382 v1

Coverage vector

measured 36 of 36 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T12:13:41.689634Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

36 of 36 outbound references displayed

verified exact0
verified fuzzy18
unresolved18
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f7675701-0880-4dd3-8d79-fd68834c0467 · outbound

This paper cites Doubleadapt: A meta-learning approach to incremental learning for stock trend forecasting,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Doubleadapt: A meta-learning approach to incremental learning for stock trend forecasting,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:42.070869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 46034437-9a6e-42ce-9604-df75a249fd5f · outbound

This paper cites Ai in finance: challenges, techniques, and opportunities,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Ai in finance: challenges, techniques, and opportunities,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:42.061653Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a8bab003-364b-4c9c-9221-88d640541b59 · outbound

This paper cites An efficient equilibrium optimizer with support vector regression for stock market prediction,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting An efficient equilibrium optimizer with support vector regression for stock market prediction,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:42.051552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b5ec1228-941c-4756-9636-9caa55e976d3 · outbound

This paper cites Accurate medium-range global weather forecasting with 3d neural networks,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Accurate medium-range global weather forecasting with 3d neural networks,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.568297Z

Source-reported events for the cited work

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Observation 3a756064-4190-4492-a1f3-a18313f3d7b3 · outbound

This paper cites Data-driven predictive control for smart hvac system in iot-integrated buildings with time-series forecasting and reinforcement learning,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Data-driven predictive control for smart hvac system in iot-integrated buildings with time-series forecasting and reinforcement learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:42.036420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 29d7ea6f-d120-4370-8038-bfa6bc4f08a3 · outbound

This paper cites Spatio-temporal meta-graph learning for traffic forecasting,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Spatio-temporal meta-graph learning for traffic forecasting,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.575541Z

Source-reported events for the cited work

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Observation 8c97193d-8696-4bcd-bcf6-656196ae5292 · outbound

This paper cites Time series prediction using deep learning methods in healthcare,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Time series prediction using deep learning methods in healthcare,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:42.018327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 38459580-55d9-48f0-9f6a-c0ad7d563022 · outbound

This paper cites Std: a seasonal-trend-dispersion decomposition of time se- ries,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Std: a seasonal-trend-dispersion decomposition of time se- ries,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:42.007188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 556cf3d6-1325-449a-beba-ec0c18739852 · outbound

This paper cites A rnn based time series approach for fore- casting turkish electricity load,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting A rnn based time series approach for fore- casting turkish electricity load,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:41.995102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a1c8f8a3-a436-405a-9d94-6e30a3cde154 · outbound

This paper cites Ngcu: A new rnn model for time-series data prediction,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Ngcu: A new rnn model for time-series data prediction,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:41.980888Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f8106c3b-811f-442f-8f49-e3b16f66e9c5 · outbound

This paper cites The performance of lstm and bilstm in forecasting time series,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting The performance of lstm and bilstm in forecasting time series,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:41.967557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 57de25f2-0c83-4c50-84ef-200fa91e4ff9 · outbound

This paper cites U- net-lstm: time series-enhanced lake boundary prediction model,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting U- net-lstm: time series-enhanced lake boundary prediction model,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:41.956320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 359bc389-f74b-4b1b-b979-9a38af04a5ce · outbound

This paper cites A cnn–lstm model for gold price time-series forecasting,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting A cnn–lstm model for gold price time-series forecasting,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:41.944944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 864392e0-2a80-40bd-b866-f61045a961ef · outbound

This paper cites Prediction for time series with cnn and lstm,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Prediction for time series with cnn and lstm,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:41.933782Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 213ab821-2d7b-4bc8-8afc-8e5d8de217a1 · outbound

This paper cites Ts2vec: Towards universal representation of time series,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Ts2vec: Towards universal representation of time series,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:41.924948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 65f7f3e7-ee3d-4699-8db3-143f603d98c1 · outbound

This paper cites T-Rep: Representation Learning for Time Series using Time-Embeddings.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting T-Rep: Representation Learning for Time Series using Time-Embeddings

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.611073Z

Source-reported events for the cited work

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Observation 6fc9a6d0-0846-4534-99ed-d0c35f12bac6 · outbound

This paper cites Simple Contrastive Representation Learning for Time Series Forecasting.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Simple Contrastive Representation Learning for Time Series Forecasting

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.615682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8b1c491-ebde-4c5a-82b4-26f85238530d · outbound

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

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Informer: Beyond efficient transformer for long sequence time-series forecasting,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.620052Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c47a6a7-20ea-4b9e-ae5f-7242e6740c9a · outbound

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

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting A Time Series is Worth 64 Words: Long-term Forecasting with Transformers

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.623528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66c708a9-6d7b-4202-96ed-e3e820c6e54e · outbound

This paper cites Carla: Self-supervised contrastive representation learning for time se- ries anomaly detection,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Carla: Self-supervised contrastive representation learning for time se- ries anomaly detection,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:41.909025Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 78532c09-7e4c-4d3b-8abe-8af5cb8d8f8e · outbound

This paper cites CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting CoST: Contrastive Learning of Disentangled Seasonal-Trend Representations for Time Series Forecasting

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.632439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 913981db-d8d2-4a35-86cc-17b527345e8e · outbound

This paper cites Fouriergnn: Rethinking multivariate time series forecast- ing from a pure graph perspective,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Fouriergnn: Rethinking multivariate time series forecast- ing from a pure graph perspective,

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.636180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d27d2c0-eaed-4198-b8b0-7ea541afe435 · outbound

This paper cites Crossgnn: Confronting noisy multivariate time series via cross interaction refinement,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Crossgnn: Confronting noisy multivariate time series via cross interaction refinement,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:41.889951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d42c854d-8389-4703-bb9d-e4d78c27e01d · outbound

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

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Crossformer: Transformer utilizing cross- dimension dependency for multivariate time series forecasting,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.644775Z

Source-reported events for the cited work

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Observation 74327b14-8a8e-469b-8389-7c77fe17d2da · outbound

This paper cites A simple framework for contrastive learning of visual representations,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting A simple framework for contrastive learning of visual representations,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.648307Z

Source-reported events for the cited work

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Observation 5c2346d6-5ebd-4928-9e37-058a742bf841 · outbound

This paper cites Unsupervised data augmentation for consistency training,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Unsupervised data augmentation for consistency training,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:41.867365Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 3a601165-89bc-41da-970f-ed4409d1bfa5 · outbound

This paper cites Graph con- trastive learning with augmentations,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Graph con- trastive learning with augmentations,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.657015Z

Source-reported events for the cited work

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Observation 693555ee-2f6b-4348-87b8-478d78f7bae4 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Momentum contrast for unsupervised visual representation learning,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.660457Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6920fd9-d66a-4748-be98-fd487e150383 · outbound

This paper cites Time-Series Representation Learning via Temporal and Contextual Contrasting.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Time-Series Representation Learning via Temporal and Contextual Contrasting

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.664166Z

Source-reported events for the cited work

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Observation 6372afc5-d9a0-40da-908f-e6258661dcfe · outbound

This paper cites Timesurl: Self-supervised contrastive learning for universal time series representation learning,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Timesurl: Self-supervised contrastive learning for universal time series representation learning,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.668451Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fab51539-b4f0-4f24-a4cb-ba7e98224531 · outbound

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

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Timesnet: Temporal 2d-variation modeling for general time series analysis,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:41.834671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ea397c29-f6cd-4527-8d9f-aa4d26dd5c25 · outbound

This paper cites Periodicity decoupling framework for long-term series forecasting,.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Periodicity decoupling framework for long-term series forecasting,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T12:13:41.820464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1c5fa8a6-ddd1-4c4a-8fe1-1f1cf0c879dc · outbound

This paper cites TSLANet: Rethinking Transformers for Time Series Representation Learning.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting TSLANet: Rethinking Transformers for Time Series Representation Learning

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.678751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f533f8d3-7c75-4929-aab9-f3935fdd4684 · outbound

This paper cites Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Unsupervised Representation Learning for Time Series with Temporal Neighborhood Coding

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.681792Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef3e3de4-0397-4b5f-b520-bc1f62717998 · outbound

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

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.685793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2e98aa04-d199-417c-a451-5e78de6dcadd · outbound

This paper cites Parametric Augmentation for Time Series Contrastive Learning.

MFF-FTNet: Multi-scale Feature Fusion across Frequency and Temporal Domains for Time Series Forecasting Parametric Augmentation for Time Series Contrastive Learning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T12:13:41.689634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

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