Cover Image

KODA: A Data-Driven Recursive Model for Time Series Forecasting and Data Assimilation using Koopman Operators

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Singh, Ashutosh, Singh, Ashish, Imbiriba, Tales, Erdogmus, Deniz, Borsoi, Ricardo
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation
by: Singh, Ashutosh, et al.
Published: (2024)

Recursive Deep Inverse Reinforcement Learning
by: Ghanem, Paul, et al.
Published: (2025)

Learning Physics Informed Neural ODEs With Partial Measurements
by: Ghanem, Paul, et al.
Published: (2024)

MarkovType: A Markov Decision Process Strategy for Non-Invasive Brain-Computer Interfaces Typing Systems
by: Sunger, Elifnur, et al.
Published: (2024)

SKOLR: Structured Koopman Operator Linear RNN for Time-Series Forecasting
by: Zhang, Yitian, et al.
Published: (2025)

Dependency-aware Maximum Likelihood Estimation for Active Learning
by: Kalkanli, Beyza, et al.
Published: (2025)

TSFMAudit: Data Contamination Auditing in Forecasting Time Series Foundation Models
by: Li, Hongkai, et al.
Published: (2026)

RPMixer: Shaking Up Time Series Forecasting with Random Projections for Large Spatial-Temporal Data
by: Yeh, Chin-Chia Michael, et al.
Published: (2024)

On the Impact of Sampling on Deep Sequential State Estimation
by: Calatrava, Helena, et al.
Published: (2023)

TimePFN: Effective Multivariate Time Series Forecasting with Synthetic Data
by: Taga, Ege Onur, et al.
Published: (2025)

Assessing the Operational Viability of Foundation Models for Time Series Forecasting
by: Soni, Kavin, et al.
Published: (2026)

Empowering Time Series Forecasting with LLM-Agents
by: Yeh, Chin-Chia Michael, et al.
Published: (2025)

Learnable Koopman-Enhanced Transformer-Based Time Series Forecasting with Spectral Control
by: Forootani, Ali, et al.
Published: (2026)

$K^2$VAE: A Koopman-Kalman Enhanced Variational AutoEncoder for Probabilistic Time Series Forecasting
by: Wu, Xingjian, et al.
Published: (2025)

ADAF: An Artificial Intelligence Data Assimilation Framework for Weather Forecasting
by: Xiang, Yanfei, et al.
Published: (2024)

PiXTime: A Model for Federated Time Series Forecasting with Heterogeneous Data across Nodes
by: Zhou, Yiming, et al.
Published: (2026)

In-Context and Few-Shots Learning for Forecasting Time Series Data based on Large Language Models
by: Gopali, Saroj, et al.
Published: (2025)

Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting
by: Yang, Yuxuan, et al.
Published: (2025)

Optimizing the Training Diet: Data Mixture Search for Robust Time Series Forecasting
by: Pennino, Federico, et al.
Published: (2025)

Tailored Architectures for Time Series Forecasting: Evaluating Deep Learning Models on Gaussian Process-Generated Data
by: Hankemeier, Victoria, et al.
Published: (2025)

IdealTSF: Can Non-Ideal Data Contribute to Enhancing the Performance of Time Series Forecasting Models?
by: Wang, Hua, et al.
Published: (2025)

Epistemic Error Decomposition for Multi-step Time Series Forecasting: Rethinking Bias-Variance in Recursive and Direct Strategies
by: Green, Riku, et al.
Published: (2025)

Low-Rank Tensor Decompositions for the Theory of Neural Networks
by: Borsoi, Ricardo, et al.
Published: (2025)

Survey and Taxonomy: The Role of Data-Centric AI in Transformer-Based Time Series Forecasting
by: Xu, Jingjing, et al.
Published: (2024)

The Rise of Diffusion Models in Time-Series Forecasting
by: Meijer, Caspar, et al.
Published: (2024)

Synthetic Series-Symbol Data Generation for Time Series Foundation Models
by: Wang, Wenxuan, et al.
Published: (2025)

PhyDA: Physics-Guided Diffusion Models for Data Assimilation in Atmospheric Systems
by: Wang, Hao, et al.
Published: (2025)

Mitigating Data Scarcity in Time Series Analysis: A Foundation Model with Series-Symbol Data Generation
by: Wang, Wenxuan, et al.
Published: (2025)

Does Synthetic Data Help? Empirical Evidence from Deep Learning Time Series Forecasters
by: Cazaux, Hugo, et al.
Published: (2026)

Towards Lightweight Time Series Forecasting: a Patch-wise Transformer with Weak Data Enriching
by: Wang, Meng, et al.
Published: (2025)

Time Series Foundation Models for Process Model Forecasting
by: Yu, Yongbo, et al.
Published: (2025)

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

MemCast: Memory-Driven Time Series Forecasting with Experience-Conditioned Reasoning
by: Tao, Xiaoyu, et al.
Published: (2026)

Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning
by: Wu, Shunyu, et al.
Published: (2025)

Time Series Forecastability Measures
by: Wang, Rui, et al.
Published: (2025)

Ellipsoidal Time Series Forecasting
by: Wang, Qilin
Published: (2025)

Performative Time-Series Forecasting
by: Zhao, Zhiyuan, et al.
Published: (2023)

FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation
by: Chen, Kun, et al.
Published: (2024)

Multistatic-Radar RCS-Signature Recognition of Aerial Vehicles: A Bayesian Fusion Approach
by: Potter, Michael, et al.
Published: (2024)

Recursive classification of satellite imaging time-series: An application to land cover mapping
by: Calatrava, Helena, et al.
Published: (2023)