A FEDformer-Based Hybrid Framework for Anomaly Detection and Risk Forecasting in Financial Time Series
Fuente:
arXiv
Guardado en:
| Autores principales: | Fan, Ziling, Liang, Ruijia, Hu, Yiwen |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB)
por: Karami, Mohammad, et al.
Publicado: (2025)
por: Karami, Mohammad, et al.
Publicado: (2025)
An Interpretable Generative Framework for Anomaly Detection in High-Dimensional Financial Time Series
por: Martinez, Waldyn G
Publicado: (2026)
por: Martinez, Waldyn G
Publicado: (2026)
Forecasting Multivariate Time Series under Predictive Heterogeneity: A Validation-Driven Clustering Framework
por: Ma, Ziling, et al.
Publicado: (2026)
por: Ma, Ziling, et al.
Publicado: (2026)
Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection
por: Khosravinia, Pooyan, et al.
Publicado: (2026)
por: Khosravinia, Pooyan, et al.
Publicado: (2026)
CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series
por: Xia, Yutong, et al.
Publicado: (2025)
por: Xia, Yutong, et al.
Publicado: (2025)
Contrastive Time Series Forecasting with Anomalies
por: Ekstrand, Joel, et al.
Publicado: (2025)
por: Ekstrand, Joel, et al.
Publicado: (2025)
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and Forecasting
por: Koumar, Josef, et al.
Publicado: (2024)
por: Koumar, Josef, et al.
Publicado: (2024)
Forecasting with Guidance: Representation-Level Supervision for Time Series Forecasting
por: Wang, Jiacheng, et al.
Publicado: (2026)
por: Wang, Jiacheng, et al.
Publicado: (2026)
RobustTSF: Towards Theory and Design of Robust Time Series Forecasting with Anomalies
por: Cheng, Hao, et al.
Publicado: (2024)
por: Cheng, Hao, et al.
Publicado: (2024)
Time Series Foundation Models for Multivariate Financial Time Series Forecasting
por: Marconi, Ben A.
Publicado: (2025)
por: Marconi, Ben A.
Publicado: (2025)
LeForecast: Enterprise Hybrid Forecast by Time Series Intelligence
por: Tan, Zheng, et al.
Publicado: (2025)
por: Tan, Zheng, et al.
Publicado: (2025)
Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt
por: Zhang, YiFan, et al.
Publicado: (2024)
por: Zhang, YiFan, et al.
Publicado: (2024)
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
por: Fu, Dongqi, et al.
Publicado: (2024)
por: Fu, Dongqi, et al.
Publicado: (2024)
When Will It Fail?: Anomaly to Prompt for Forecasting Future Anomalies in Time Series
por: Park, Min-Yeong, et al.
Publicado: (2025)
por: Park, Min-Yeong, et al.
Publicado: (2025)
TAB: Unified Benchmarking of Time Series Anomaly Detection Methods
por: Qiu, Xiangfei, et al.
Publicado: (2025)
por: Qiu, Xiangfei, et al.
Publicado: (2025)
Adaptive Multi-Scale Decomposition Framework for Time Series Forecasting
por: Hu, Yifan, et al.
Publicado: (2024)
por: Hu, Yifan, et al.
Publicado: (2024)
ForecastGAN: A Decomposition-Based Adversarial Framework for Multi-Horizon Time Series Forecasting
por: Fatima, Syeda Sitara Wishal, et al.
Publicado: (2025)
por: Fatima, Syeda Sitara Wishal, et al.
Publicado: (2025)
A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection
por: Jin, Ming, et al.
Publicado: (2023)
por: Jin, Ming, et al.
Publicado: (2023)
Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly Detection
por: Srinivasan, Abhishek, et al.
Publicado: (2025)
por: Srinivasan, Abhishek, et al.
Publicado: (2025)
Graph Anomaly Detection in Time Series: A Survey
por: Ho, Thi Kieu Khanh, et al.
Publicado: (2023)
por: Ho, Thi Kieu Khanh, et al.
Publicado: (2023)
Supervised Autoencoder MLP for Financial Time Series Forecasting
por: Bieganowski, Bartosz, et al.
Publicado: (2024)
por: Bieganowski, Bartosz, et al.
Publicado: (2024)
KDSelector: A Knowledge-Enhanced and Data-Efficient Model Selector Learning Framework for Time Series Anomaly Detection
por: Liang, Zhiyu, et al.
Publicado: (2025)
por: Liang, Zhiyu, et al.
Publicado: (2025)
Matrix Profile for Anomaly Detection on Multidimensional Time Series
por: Yeh, Chin-Chia Michael, et al.
Publicado: (2024)
por: Yeh, Chin-Chia Michael, et al.
Publicado: (2024)
A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series
por: Deng, Ziquan, et al.
Publicado: (2024)
por: Deng, Ziquan, et al.
Publicado: (2024)
Bridging Time and Frequency: A Joint Modeling Framework for Irregular Multivariate Time Series Forecasting
por: Qiu, Xiangfei, et al.
Publicado: (2026)
por: Qiu, Xiangfei, et al.
Publicado: (2026)
SST: Multi-Scale Hybrid Mamba-Transformer Experts for Time Series Forecasting
por: Xu, Xiongxiao, et al.
Publicado: (2024)
por: Xu, Xiongxiao, et al.
Publicado: (2024)
Deep Learning-Based Financial Time Series Forecasting via Sliding Window and Variational Mode Decomposition
por: Li, Luke
Publicado: (2025)
por: Li, Luke
Publicado: (2025)
CALM: A Framework for Continuous, Adaptive, and LLM-Mediated Anomaly Detection in Time-Series Streams
por: Devireddy, Ashok, et al.
Publicado: (2025)
por: Devireddy, Ashok, et al.
Publicado: (2025)
Can Multimodal LLMs Perform Time Series Anomaly Detection?
por: Xu, Xiongxiao, et al.
Publicado: (2025)
por: Xu, Xiongxiao, et al.
Publicado: (2025)
FinTSB: A Comprehensive and Practical Benchmark for Financial Time Series Forecasting
por: Hu, Yifan, et al.
Publicado: (2025)
por: Hu, Yifan, et al.
Publicado: (2025)
Multi-period Learning for Financial Time Series Forecasting
por: Zhang, Xu, et al.
Publicado: (2025)
por: Zhang, Xu, et al.
Publicado: (2025)
Weighted Contrastive Learning for Anomaly-Aware Time-Series Forecasting
por: Ekstrand, Joel, et al.
Publicado: (2025)
por: Ekstrand, Joel, et al.
Publicado: (2025)
Feature Selection with Annealing for Forecasting Financial Time Series
por: Pabuccu, Hakan, et al.
Publicado: (2023)
por: Pabuccu, Hakan, et al.
Publicado: (2023)
Online Time Series Forecasting with Theoretical Guarantees
por: Li, Zijian, et al.
Publicado: (2025)
por: Li, Zijian, et al.
Publicado: (2025)
iTFKAN: Interpretable Time Series Forecasting with Kolmogorov-Arnold Network
por: Liang, Ziran, et al.
Publicado: (2025)
por: Liang, Ziran, et al.
Publicado: (2025)
Integrating Inductive Biases in Transformers via Distillation for Financial Time Series Forecasting
por: Den, Yu-Chen, et al.
Publicado: (2026)
por: Den, Yu-Chen, et al.
Publicado: (2026)
KARMA: A Multilevel Decomposition Hybrid Mamba Framework for Multivariate Long-Term Time Series Forecasting
por: Ye, Hang, et al.
Publicado: (2025)
por: Ye, Hang, et al.
Publicado: (2025)
Learning Multi-Pattern Normalities in the Frequency Domain for Efficient Time Series Anomaly Detection
por: Chen, Feiyi, et al.
Publicado: (2023)
por: Chen, Feiyi, et al.
Publicado: (2023)
Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach
por: Liu, Shun, et al.
Publicado: (2023)
por: Liu, Shun, et al.
Publicado: (2023)
TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
por: Si, Haotian, et al.
Publicado: (2024)
por: Si, Haotian, et al.
Publicado: (2024)
Ejemplares similares
-
A Comprehensive Forecasting-Based Framework for Time Series Anomaly Detection: Benchmarking on the Numenta Anomaly Benchmark (NAB)
por: Karami, Mohammad, et al.
Publicado: (2025) -
An Interpretable Generative Framework for Anomaly Detection in High-Dimensional Financial Time Series
por: Martinez, Waldyn G
Publicado: (2026) -
Forecasting Multivariate Time Series under Predictive Heterogeneity: A Validation-Driven Clustering Framework
por: Ma, Ziling, et al.
Publicado: (2026) -
Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection
por: Khosravinia, Pooyan, et al.
Publicado: (2026) -
CaPulse: Detecting Anomalies by Tuning in to the Causal Rhythms of Time Series
por: Xia, Yutong, et al.
Publicado: (2025)