Non-Stationary Time Series

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3 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detection
Oct 7, 2026Xudong Mou, Tiejun Wang, Rui Wang +5Time-Series Anomaly Detection Non-Stationary Time Series
Beihang University, Beijing, China · Beihang Hangzhou Innovation Institute, Hangzhou, China · China University of Geosciences (Beijing), Beijing, China
StaFIR: Convex Learning of Stationarity-Aware Causal Filters
Oct 5, 2026Lorena Egger, Mathis LingerTime Series Forecasting Financial Time Series
Drakai Capital Paris
In a Streaming World, Should You Stand Still? A Comprehensive Benchmark of Anomaly Detection in Streams
Sep 30, 2026Magali Parrino, Antoine Ajenjo, Emmanuel Remy +3Online Anomaly Detection Concept Drift
EDF R&D Chatou, France · ENS, PSL University, CNRS, Inria Paris, France · DI ENS, ENS, PSL University, CNRS, Inria Paris, France
Cyclostationary Phase Conditioning for Medical Time Series Diffusion
Sep 28, 2026Samuel Ruiperez-Campillo, Michele Copetti, Jorge da Silva Goncalves +3Time Series Imputation Diffusion-Based Inverse Problems
Dept. of Computer Science ETH Zurich
DrafTS: Time-Aware Decomposition with Residual Correction for Time Series Modeling
Sep 27, 2026Yiqiu Liu, Siru Zhong, Zhiguang Wang +2Time Series Decomposition Non-Stationary Time Series
The Hong Kong University of Science and Technology (Guangzhou), China · Abel AI Lab, USA · Squirrel Ai Learning, USA
Identifiable Markov Switching Models with Instantaneous Effects and Exponential Families
Jun 1, 2026Roel Hulsman, Carles Balsells-Rodas, Sara MagliacaneTime-Series Causal Discovery Regime-Switching Dynamical Systems
University of Amsterdam · 2Adyen · 3Imperial College London +1
MedMamba: Multi-View State Space Models with Adaptive Graph Learning for Medical Time Series Classification
May 24, 2026Da Zhang, Bingyu Li, Zhiyuan Zhao +3Graph Structure Learning Selective SSMs
Northwest Polytechnical University · TeleAI, China Telecom · TeleAI +1
Time Segmented Beamforming via Dynamic Programming: Theory and Implementation
May 24, 2026Manan Mittal, Ryan M. Corey, Diego Cuji +2Dynamic Programming Non-Stationary Time Series
Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, NY 11794 USA · Department of Electrical and Computer Engineering, University of Illinois, Chicago, IL 60607 USA · Department of Electrical and Computer Engineering, University of Massachusetts Dartmouth, Dartmouth, MA 02747 USA +1
Learning Time-Inhomogeneous Markov Dynamics in Financial Time Series via Neural Parameterization
May 6, 2026Jan Rovirosa, Jesse SchmolzeMarkov Models Financial Time Series
University of Wisconsin – Madison, Madison, WI, USA
Dynamic Vine Copulas: Detecting and Quantifying Time-Varying Higher-Order Interactions
May 4, 2026Houman Safaai, Alessandro Marin VargasCopula Models Non-Stationary Time Series
Kempner Institute at Harvard University · Stanford University
PAMod: Modeling Cyclical Shifts via Phase-Amplitude Modulation for Non-stationary Time Series Forecasting
May 1, 2026Yingbo Zhou, Yutong Ye, Shuhao Li +5Distribution Shift Time Series Forecasting
Fudan University · Beihang University · Beijing University of Posts and Telecommunications
TTCD:Transformer Integrated Temporal Causal Discovery from Non-Stationary Time Series Data
Apr 27, 2026Omar Faruque, Sahara Ali, Xue Zheng +1Time-Series Causal Discovery Causal Structure Learning
Department of Information Systems University of Maryland, Baltimore County Baltimore, Maryland, USA · Department of Data Science University of North Texas Denton, Texas, USA · Climate Science Section Lawrence Livermore National Laboratory Livermore, California, USA
Robust Streaming PCA
Feb 8, 2019Daniel Bienstock, Minchan Jeong, Apurv Shukla +1Principal Component Analysis Streaming Algorithms
Graduate School of AI KAIST
Cyclic MPDR Beamforming for Suppression of Almost-Cyclostationary Acoustic Interference
Date pendingGiovanni Bologni, Martin Bo M\oller, Richard Heusdens +1Speech Enhancement Non-Stationary Time Series
Signal Processing Systems Group, Delft University of Technology, Delft, The Netherlands · Bang & Olufsen, Struer, 7600, Denmark · Netherlands Defence Academy, 1781 AC Den Helder, The Netherlands

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MORA: Modeling Observed Changes for Drift-Robust Time-Series Anomaly Detection

Xudong Mou, Tiejun Wang, Rui Wang, Hui Wang+4

Beihang University, Beijing, China · Beihang Hangzhou Innovation Institute, Hangzhou, China · China University of Geosciences (Beijing), Beijing, China

Time-Series Anomaly Detection Non-Stationary Time Series Reconstruction-Based Anomaly Detection

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Time-series anomaly detection (TSAD) identifies deviations from patterns learned from historical data. In non-stationary settings, distribution drift and true anomalies can cause similar local changes, making it difficult to tell whether a deviation reflects abnormality or evolving context. Existing methods typically adapt to detected shifts or learn drift-insensitive representations, but do not resolve this ambiguity. We define this problem as \emph{temporal change disambiguation}: determining whether a local deviation is explained by broader temporal evolution. We introduce MORA, a drift-robust TSAD framework that reconstructs the same local target from paired short- and long-term views. The reconstruction gap measures contextual support for a local deviation, and a data-dependent correction mechanism conservatively adjusts the primary local anomaly score. Context can only reduce the score when it improves reconstruction of the same target. MORA needs neither drift annotations nor online adaptation. Experiments on four TSAD benchmarks show strong robustness to non-stationarity while preserving sensitivity to genuine anomalies.

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