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

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

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

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

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

Reference 2

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

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

Reference 3

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

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

Reference 4

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

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

Reference 5

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

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

Reference 6

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

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

Reference 7

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This paper cites Attention is all you need.

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

Reference 8

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

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

Reference 9

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This paper cites Music Transformer.

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

Reference 10

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

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

Reference 11

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

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

Reference 12

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

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

Reference 13

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

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

Reference 14

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

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

Reference 15

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

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

Reference 16

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

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

Reference 17

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

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

Reference 18

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

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

Reference 19

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

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

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

Reference 20

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

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

Reference 21

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

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

Reference 22

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

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

Reference 23

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

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

Reference 24

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

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

Reference 25

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

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

Reference 26

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

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

Reference 27

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

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

Reference 28

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

Reference 29

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

Reference 30

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

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

Reference 31

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

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

Reference 32

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

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

Reference 33

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

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

Reference 34

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

Reference 35

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

Reference 36

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

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

Reference 37

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This paper cites Uci machine learning repository.

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

Reference 38

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

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

Reference 39

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This paper cites Zhang, and JUN ZHOU.

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

Reference 40

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

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

Reference 41

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This paper cites No free lunch theorems for optimization.

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

Reference 42

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

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

Reference 43

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This paper cites Transferability in Deep Learning: A Survey.

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

Reference 44

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

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

Reference 45

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

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

Reference 46

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

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

Reference 47

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This paper cites Learning to embed time series patches independently.

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

Reference 48

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

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

Reference 49

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

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

Reference 50

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

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

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

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

Reference 51

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

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

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

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

Reference 52

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

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

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

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

Reference 2024

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

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No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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