Cover Image

Shifting the Paradigm: A Diffeomorphism Between Time Series Data Manifolds for Achieving Shift-Invariancy in Deep Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Demirel, Berken Utku, Holz, Christian
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning Without Augmenting: Unsupervised Time Series Representation Learning via Frame Projections
by: Demirel, Berken Utku, et al.
Published: (2025)

An Unsupervised Approach for Periodic Source Detection in Time Series
by: Demirel, Berken Utku, et al.
Published: (2024)

Temporal Cardiovascular Dynamics for Improved PPG-Based Heart Rate Estimation
by: Demirel, Berken Utku, et al.
Published: (2025)

WildPPG: A Real-World PPG Dataset of Long Continuous Recordings
by: Meier, Manuel, et al.
Published: (2024)

Tri-Spectral PPG: Robust Reflective Photoplethysmography by Fusing Multiple Wavelengths for Cardiac Monitoring
by: Meier, Manuel, et al.
Published: (2024)

FW-NKF: Frequency-Weighted Neural Kalman Filters
by: Dogan, Adnan Harun, et al.
Published: (2026)

Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction
by: Dharmakeerthi, Kulunu, et al.
Published: (2024)

Beyond Subjectivity: Continuous Cybersickness Detection Using EEG-based Multitaper Spectrum Estimation
by: Demirel, Berken Utku, et al.
Published: (2025)

Handling Distribution Shifts on Graphs: An Invariance Perspective
by: Wu, Qitian, et al.
Published: (2022)

Meta-learning to Address Data Shift in Time Series Classification
by: Myren, Samuel, et al.
Published: (2026)

Continual Learning as Shared-Manifold Continuation Under Compatible Shift
by: Kobs, Henry J.
Published: (2026)

RocketStack: Level-aware Deep Recursive Ensemble Learning Architecture
by: Demirel, Çağatay
Published: (2025)

Detecting Diffusion-Generated Time Series Under Generator Shift
by: Soi, Zhi Wen, et al.
Published: (2026)

Data-Specific Hyper-Parameter Design: A Paradigm Shift in Reservoir Computing
by: Manjunath, G, et al.
Published: (2026)

Benchmarking Distribution Shift in Tabular Data with TableShift
by: Gardner, Josh, et al.
Published: (2023)

Addressing Concept Shift in Online Time Series Forecasting: Detect-then-Adapt
by: Zhang, YiFan, et al.
Published: (2024)

Analyzing Deep Transformer Models for Time Series Forecasting via Manifold Learning
by: Kaufman, Ilya, et al.
Published: (2024)

Learning Pattern-Specific Experts for Time Series Forecasting Under Patch-level Distribution Shift
by: Sun, Yanru, et al.
Published: (2024)

CATS: Mitigating Correlation Shift for Multivariate Time Series Classification
by: Lin, Xiao, et al.
Published: (2025)

The Forecast After the Forecast: A Post-Processing Shift in Time Series
by: Liang, Daojun, et al.
Published: (2026)

Don't Think It Twice: Exploit Shift Invariance for Efficient Online Streaming Inference of CNNs
by: Kechris, Christodoulos, et al.
Published: (2024)

Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems
by: Wang, Xiaofei, et al.
Published: (2024)

APT: Affine Prototype-Timestamp For Time Series Forecasting Under Distribution Shift
by: Li, Yujie, et al.
Published: (2025)

Evolving Multi-Scale Normalization for Time Series Forecasting under Distribution Shifts
by: Qin, Dalin, et al.
Published: (2024)

The Majority Vote Paradigm Shift: When Popular Meets Optimal
by: Purificato, Antonio, et al.
Published: (2025)

Understand the Effect of Importance Weighting in Deep Learning on Dataset Shift
by: Vo, Thien Nhan
Published: (2025)

Causal Time Series Modeling of Supraglacial Lake Evolution in Greenland under Distribution Shift
by: Hossain, Emam, et al.
Published: (2025)

"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts
by: Babbar, Varun, et al.
Published: (2024)

Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams
by: Wojak-Strzelecka, Natalia, et al.
Published: (2026)

Fairness Under Group-Conditional Prior Probability Shift: Invariance, Drift, and Target-Aware Post-Processing
by: Asiaee, Amir, et al.
Published: (2026)

Calibration of Time-Series Forecasting: Detecting and Adapting Context-Driven Distribution Shift
by: Chen, Mouxiang, et al.
Published: (2023)

Rethinking Adam for Time Series Forecasting: A Simple Heuristic to Improve Optimization under Distribution Shifts
by: Dong, Yuze, et al.
Published: (2026)

Adapting to Online Distribution Shifts in Deep Learning: A Black-Box Approach
by: Baby, Dheeraj, et al.
Published: (2025)

Improving Shift Invariance in Convolutional Neural Networks with Translation Invariant Polyphase Sampling
by: Saha, Sourajit, et al.
Published: (2024)

Linear Mode Connectivity under Data Shifts for Deep Ensembles of Image Classifiers
by: Hepburn, C., et al.
Published: (2025)

Monitor and Recover: A Paradigm for Future Research on Distribution Shift in Learning-Enabled Cyber-Physical Systems
by: Lin, Vivian, et al.
Published: (2025)

Weighted Risk Invariance: Domain Generalization under Invariant Feature Shift
by: Wong, Gina, et al.
Published: (2024)

The Perspectivist Paradigm Shift: Assumptions and Challenges of Capturing Human Labels
by: Fleisig, Eve, et al.
Published: (2024)

Lost in Time? A Meta-Learning Framework for Time-Shift-Tolerant Physiological Signal Transformation
by: Hong, Qian, et al.
Published: (2025)

Frequency Matters: When Time Series Foundation Models Fail Under Spectral Shift
by: Wang, Tianze, et al.
Published: (2025)