Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting | Paper Citation Record

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting write newline

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This paper cites https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting https://archive.ics.uci.edu/ml/datasets/ElectricityLoadDiagrams20112014

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

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting http://pems.dot.ca.gov/

Reference 3

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Unresolved cited work

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This paper cites Chronos: Learning the Language of Time Series.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Chronos: Learning the Language of Time Series

Reference 5

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This paper cites Pagerank bandits for link prediction.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Pagerank bandits for link prediction

Reference 6

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This paper cites Adaptive test-time personalization for federated learning.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Adaptive test-time personalization for federated learning

Reference 7

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This paper cites Matcha: Mitigating graph structure shifts with test-time adaptation.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Matcha: Mitigating graph structure shifts with test-time adaptation

Reference 8

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This paper cites Tsfel: Time series feature extraction library.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Tsfel: Time series feature extraction library

Reference 9

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This paper cites Ensemble selection from libraries of models.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Ensemble selection from libraries of models

Reference 10

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This paper cites F., Skabardonis, A., Varaiya, P.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting F., Skabardonis, A., Varaiya, P

Reference 11

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Unresolved cited work

Reference 12

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This paper cites E., and Shah, K.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting E., and Shah, K

Reference 13

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This paper cites I., and Chen, H.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting I., and Chen, H

Reference 14

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This paper cites Adversarial graph contrastive learning with information regularization.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Adversarial graph contrastive learning with information regularization

Reference 15

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This paper cites Auto-sklearn 2.0: Hands-free automl via meta-learning.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Auto-sklearn 2.0: Hands-free automl via meta-learning

Reference 16

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This paper cites Unsupervised scalable representation learning for multivariate time series.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Unsupervised scalable representation learning for multivariate time series

Reference 17

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This paper cites I., and He, J.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting I., and He, J

Reference 18

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This paper cites What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting What Do LLMs Need to Understand Graphs: A Survey of Parametric Representation of Graphs

Reference 19

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Vcr-graphormer: A mini-batch graph transformer via virtual connections

Reference 20

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This paper cites Generating fine-grained causality in climate time series data for forecasting and anomaly detection.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Generating fine-grained causality in climate time series data for forecasting and anomaly detection

Reference 21

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This paper cites ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting ClimateBench-M: A Multi-Modal Climate Data Benchmark with a Simple Generative Method

Reference 22

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This paper cites On the sensitivity of individual fairness: Measures and robust algorithms.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting On the sensitivity of individual fairness: Measures and robust algorithms

Reference 23

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This paper cites Temporal heterogeneous graph generation with privacy, utility, and efficiency.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Temporal heterogeneous graph generation with privacy, utility, and efficiency

Reference 24

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This paper cites and Fulcher, B.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting and Fulcher, B

Reference 25

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Unresolved cited work

Reference 26

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This paper cites A review on time series aggregation methods for energy system models.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting A review on time series aggregation methods for energy system models

Reference 27

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Unresolved cited work

Reference 28

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This paper cites Network of tensor time series.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Network of tensor time series

Reference 29

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This paper cites Retrieval Based Time Series Forecasting.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Retrieval Based Time Series Forecasting

Reference 30

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Towards editing time series

Reference 31

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This paper cites Automated contrastive learning strategy search for time series.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Automated contrastive learning strategy search for time series

Reference 32

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This paper cites Causality-aware spatiotemporal graph neural networks for spatiotemporal time series imputation.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Causality-aware spatiotemporal graph neural networks for spatiotemporal time series imputation

Reference 33

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Adam: A Method for Stochastic Optimization

Reference 34

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting K., and Crone, S

Reference 35

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This paper cites Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

Reference 36

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This paper cites Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

Reference 37

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Modeling long-and short-term temporal patterns with deep neural networks

Reference 38

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This paper cites Trend modeling for traffic time series analysis: An integrated study.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Trend modeling for traffic time series analysis: An integrated study

Reference 39

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This paper cites Everything evolves in personalized pagerank.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Everything evolves in personalized pagerank

Reference 40

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting F., Tong, H., and He, J

Reference 41

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting and Zohren, S

Reference 42

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Backtime: Backdoor attacks on multivariate time series forecasting

Reference 43

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Observation 36d01a84-2bed-413b-beb5-2bf416e15f62 · outbound

This paper cites CATS: Mitigating Correlation Shift for Multivariate Time Series Classification.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting CATS: Mitigating Correlation Shift for Multivariate Time Series Classification

Reference 44

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Non-stationary transformers: Rethinking the stationarity in time series forecasting

Reference 45

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

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting itransformer: Inverted transformers are effective for time series forecasting

Reference 46

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This paper cites Self-paced ensemble for highly imbalanced massive data classification.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Self-paced ensemble for highly imbalanced massive data classification

Reference 47

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Mesa: boost ensemble imbalanced learning with meta-sampler

Reference 48

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This paper cites IMBENS: Ensemble Class-imbalanced Learning in Python.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting IMBENS: Ensemble Class-imbalanced Learning in Python

Reference 49

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Class-imbalanced graph learning without class rebalancing

Reference 50

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Unresolved cited work

Reference 51

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This paper cites H., Sinthong, P., and Kalagnanam, J.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting H., Sinthong, P., and Kalagnanam, J

Reference 52

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Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting and Torgo, L

Reference 53

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This paper cites Pytorch: An imperative style, high-performance deep learning library.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Pytorch: An imperative style, high-performance deep learning library

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This paper cites and Tong, H.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting and Tong, H

Reference 55

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This paper cites DIMES : A differentiable meta solver for combinatorial optimization problems.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting DIMES : A differentiable meta solver for combinatorial optimization problems

Reference 56

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This paper cites V., Zhang, Y., and Tong, H.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting V., Zhang, Y., and Tong, H

Reference 57

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This paper cites TUCKET : A tensor time series data structure for efficient and accurate factor analysis over time ranges.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting TUCKET : A tensor time series data structure for efficient and accurate factor analysis over time ranges

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:02.051306Z

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This paper cites Ask, and it shall be given: On the Turing completeness of prompting.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Ask, and it shall be given: On the Turing completeness of prompting

Reference 59

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Observation 30d9c9c0-b689-4a93-92d7-6778f68f5276 · outbound

This paper cites How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting How Efficient is LLM-Generated Code? A Rigorous & High-Standard Benchmark

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:56.978627Z

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Observation d6fb91c3-3281-45df-bec0-779770c0e917 · outbound

This paper cites Canon: Complex analytics of network of networks for modeling adversarial activities.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Canon: Complex analytics of network of networks for modeling adversarial activities

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:02.030813Z

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Observation 54e69b4f-bd3a-44d3-9b56-edfd8d9d0438 · outbound

This paper cites and Rokach, L.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting and Rokach, L

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:02.013738Z

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Observation 85c4a863-a246-49ee-a7c9-34fde1da6831 · outbound

This paper cites B., Gudelek, M.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting B., Gudelek, M

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.996655Z

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Observation 3a66e1ca-75ae-4e0e-aeb9-ee4def117b2a · outbound

This paper cites C., Erickson, N., Shen, H., Shirkov, A., Hu, T., and Wang, B.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting C., Erickson, N., Shen, H., Shirkov, A., Hu, T., and Wang, B

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.980387Z

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Observation 4a332657-16a7-4133-be86-23cae781379c · outbound

This paper cites A., Gupta, V., Althoff, T., and Hartvigsen, T.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting A., Gupta, V., Althoff, T., and Hartvigsen, T

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.960232Z

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Observation e4a190e0-6e46-4d9d-95e8-8573f8eb295d · outbound

This paper cites F., and He, J.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting F., and He, J

Reference 66

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Observation 2e53594c-6a19-4423-b4d8-42d58cbdbda8 · outbound

This paper cites Invariant link selector for spatial-temporal out-of-distribution problem.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Invariant link selector for spatial-temporal out-of-distribution problem

Reference 67

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Observation a74b3981-5373-4682-a5aa-e180b5d41660 · outbound

This paper cites Networked time series imputation via position-aware graph enhanced variational autoencoders.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Networked time series imputation via position-aware graph enhanced variational autoencoders

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.912198Z

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Observation c0350d8c-80b9-4cef-8e58-bbae253492fb · outbound

This paper cites Learning graph quantized tokenizers.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Learning graph quantized tokenizers

Reference 69

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Observation b0d6168a-32db-4010-a837-396bf38b325c · outbound

This paper cites Y., and ZHOU, J.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Y., and ZHOU, J

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.876472Z

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Observation f6dee87f-e432-4974-b2d5-65e75692e792 · outbound

This paper cites Deep Time Series Models: A Comprehensive Survey and Benchmark.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Deep Time Series Models: A Comprehensive Survey and Benchmark

Reference 71

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Observation eb829cbb-79c1-4af6-9eb4-27d6e8f30c66 · outbound

This paper cites TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables

Reference 72

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Observation 00685dd8-5cca-4563-ba6c-9bed286d1207 · outbound

This paper cites and He, J.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting and He, J

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.859445Z

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Observation 1145416a-32f5-4eb9-9fdd-54e8c260fe28 · outbound

This paper cites Fast adaptation for cold-start collaborative filtering with meta-learning.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Fast adaptation for cold-start collaborative filtering with meta-learning

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.841037Z

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Observation 0aa419d5-92ba-4ca5-807b-fba1ac5ab93f · outbound

This paper cites Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.821732Z

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Observation 33269198-a2fd-4f4f-9a9f-32069c05a4cf · outbound

This paper cites Augmentations in hypergraph contrastive learning: Fabricated and generative.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Augmentations in hypergraph contrastive learning: Fabricated and generative

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.804215Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2367c1d8-09ca-4ca4-a527-d2b42298960a · outbound

This paper cites Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyond

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:58.848362Z

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Observation 83afccc1-ebec-4735-8414-570cddbf4829 · outbound

This paper cites Robust watermarking for diffusion models: A unified multi-dimensional recipe, 2024 b.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Robust watermarking for diffusion models: A unified multi-dimensional recipe, 2024 b

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.785797Z

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Observation 7c68ff39-fec8-40d0-9c73-1af1f5b8e59c · outbound

This paper cites Connecting domains and contrasting samples: A ladder for domain generalization.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Connecting domains and contrasting samples: A ladder for domain generalization

Reference 79

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Observation dd7f728b-178c-4bdb-8d53-b4179ccc1127 · outbound

This paper cites Autoformer: Decomposition transformers with Auto-Correlation for long-term series forecasting.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Autoformer: Decomposition transformers with Auto-Correlation for long-term series forecasting

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.767267Z

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Observation be6d715f-12a6-4061-b23a-1cbbd4c59d67 · outbound

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

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting TimesNet : Temporal 2d-variation modeling for general time series analysis

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.749419Z

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Observation f078309e-e3d3-4230-bb85-47a3adee5e64 · outbound

This paper cites Fair Anomaly Detection For Imbalanced Groups.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Fair Anomaly Detection For Imbalanced Groups

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-07T14:34:59.569457Z

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This paper cites F., Han, J., and Tong, H.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting F., Han, J., and Tong, H

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.730769Z

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Observation 63b023a5-cc58-4366-a92b-799603ee776b · outbound

This paper cites Language models are graph learners.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Language models are graph learners

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.711629Z

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Observation 4f8fc758-a59c-488a-a6b0-8fe8c0bbfafd · outbound

This paper cites Discrete-state Continuous-time Diffusion for Graph Generation.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Discrete-state Continuous-time Diffusion for Graph Generation

Reference 85

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Observation da5114aa-e42b-48a7-80a6-06aaba809436 · outbound

This paper cites Dynamic knowledge graph alignment.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Dynamic knowledge graph alignment

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.696414Z

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Observation cc895313-ea16-4c36-9bc4-36cd3b0852c2 · outbound

This paper cites Bright: A bridging algorithm for network alignment.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Bright: A bridging algorithm for network alignment

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.681118Z

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Observation e35d4990-e3a2-4fb0-ad97-f5f9efcd78d9 · outbound

This paper cites Dissecting cross-layer dependency inference on multi-layered inter-dependent networks.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Dissecting cross-layer dependency inference on multi-layered inter-dependent networks

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.662738Z

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Observation 3e0fbb48-592b-4cd8-8e0d-c4f5d3f4ec1c · outbound

This paper cites From trainable negative depth to edge heterophily in graphs.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting From trainable negative depth to edge heterophily in graphs

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.645928Z

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Observation b9ab6e76-43e4-438f-97bb-8ebfcbe53131 · outbound

This paper cites Reconciling competing sampling strategies of network embedding.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Reconciling competing sampling strategies of network embedding

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.628400Z

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Observation b832d4ac-f511-4c7e-babe-82d3ffe72cda · outbound

This paper cites THeGCN: Temporal Heterophilic Graph Convolutional Network.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting THeGCN: Temporal Heterophilic Graph Convolutional Network

Reference 91

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Observation 7d4e19c9-b1d4-4cce-9093-4bbb3825a990 · outbound

This paper cites Pacer: Network embedding from positional to structural.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Pacer: Network embedding from positional to structural

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.612470Z

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Observation 286be7ab-1848-41ed-94c1-2bac6449e83c · outbound

This paper cites Topological anonymous walk embedding: A new structural node embedding approach.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Topological anonymous walk embedding: A new structural node embedding approach

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.596671Z

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Observation d242a725-ff43-473e-b5b5-6d2710e7c676 · outbound

This paper cites M., Bian, J., Chang, Y., Lurie, J.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting M., Bian, J., Chang, Y., Lurie, J

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.574062Z

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Observation 638d6e93-280b-43b6-9335-e0c9b689a39a · outbound

This paper cites Frequency-domain mlps are more effective learners in time series forecasting.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Frequency-domain mlps are more effective learners in time series forecasting

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.555903Z

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Observation 35e1f815-38a2-4297-b760-5571f9cf29a3 · outbound

This paper cites Ensuring user-side fairness in dynamic recommender systems.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Ensuring user-side fairness in dynamic recommender systems

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.537805Z

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Observation ca327829-9870-4cfe-b441-93c23df536f5 · outbound

This paper cites Embracing plasticity: Balancing stability and plasticity in continual recommender systems.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Embracing plasticity: Balancing stability and plasticity in continual recommender systems

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.520913Z

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Observation c798d20c-5fec-4b08-9d41-c85f66d29585 · outbound

This paper cites Generalizable recommender system during temporal popularity distribution shifts.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Generalizable recommender system during temporal popularity distribution shifts

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.506169Z

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Observation e3f4f3d7-2383-4343-b661-28a503051b47 · outbound

This paper cites Ensemble forecasting for complex time series using sparse representation and neural networks.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting Ensemble forecasting for complex time series using sparse representation and neural networks

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.489476Z

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Observation 5c60496a-6e0d-462a-9460-64c436373c68 · outbound

This paper cites and Li, G.

Breaking Silos: Adaptive Model Fusion Unlocks Better Time Series Forecasting and Li, G

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:35:01.474452Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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