Xiangfei Qiu

Orcid: 0009-0000-4318-3925

According to our database1, Xiangfei Qiu authored at least 21 papers between 2024 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2025
DBLoss: Decomposition-based Loss Function for Time Series Forecasting.
CoRR, October, 2025

An Encode-then-Decompose Approach to Unsupervised Time Series Anomaly Detection on Contaminated Training Data-Extended Version.
CoRR, October, 2025

Enhancing Time Series Forecasting through Selective Representation Spaces: A Patch Perspective.
CoRR, October, 2025

Multi-Scale Spatial-Temporal Hypergraph Network with Lead-Lag Structures for Stock Time Series Forecasting.
CoRR, September, 2025

ASTGI: Adaptive Spatio-Temporal Graph Interactions for Irregular Multivariate Time Series Forecasting.
CoRR, September, 2025

Unlocking the Power of Mixture-of-Experts for Task-Aware Time Series Analytics.
CoRR, September, 2025

DAG: A Dual Causal Network for Time Series Forecasting with Exogenous Variables.
CoRR, September, 2025

TAB: Unified Benchmarking of Time Series Anomaly Detection Methods.
Proc. VLDB Endow., May, 2025

K<sup>2</sup>VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting.
CoRR, May, 2025

Rethinking Irregular Time Series Forecasting: A Simple yet Effective Baseline.
CoRR, May, 2025

A Comprehensive Survey of Deep Learning for Multivariate Time Series Forecasting: A Channel Strategy Perspective.
CoRR, February, 2025

DUET: Dual Clustering Enhanced Multivariate Time Series Forecasting.
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.1, 2025

SSD-TS: Exploring the Potential of Linear State Space Models for Diffusion Models in Time Series Imputation.
Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining, V.2, 2025

K2VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting.
Proceedings of the Forty-second International Conference on Machine Learning, 2025

CATCH: Channel-Aware Multivariate Time Series Anomaly Detection via Frequency Patching.
Proceedings of the Thirteenth International Conference on Learning Representations, 2025

EasyTime: Time Series Forecasting Made Easy.
Proceedings of the 41st IEEE International Conference on Data Engineering, 2025

2024
AutoCTS++: zero-shot joint neural architecture and hyperparameter search for correlated time series forecasting.
VLDB J., September, 2024

TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods.
Proc. VLDB Endow., May, 2024

MultiRC: Joint Learning for Time Series Anomaly Prediction and Detection with Multi-scale Reconstructive Contrast.
CoRR, 2024

DiffImp: Efficient Diffusion Model for Probabilistic Time Series Imputation with Bidirectional Mamba Backbone.
CoRR, 2024

FoundTS: Comprehensive and Unified Benchmarking of Foundation Models for Time Series Forecasting.
CoRR, 2024


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