GenTS is a modular benchmark library providing unified data pipelines, generative models, and evaluation metrics for time series synthesis, forecasting, and imputation, with open-source code and initial benchmarking experiments.
Timevae: A variational auto-encoder for multivariate time series generation
6 Pith papers cite this work. Polarity classification is still indexing.
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SDFlow learns a global transport map via similarity-driven flow matching in VQ latent space, using low-rank manifold decomposition and a categorical posterior to handle discreteness, yielding SOTA long-horizon performance and inference speedups.
GCGNet uses a variational generator, graph structure aligner, and graph refiner to jointly capture temporal and channel correlations in time series forecasting with exogenous variables, outperforming baselines on 12 real-world datasets.
CAPS provides an iterative differentially private synthesis method that outperforms one-shot baselines on authentic educational real-world data.
AutoReproduce is a multi-agent system using paper lineage to autonomously reproduce AI experiment code, with a new benchmark showing improvements over baselines in fidelity and execution.
MSDformer introduces a multi-scale discrete transformer that tokenizes time series at multiple scales and models them autoregressively in discrete space, claiming superior performance over prior DTM methods with rate-distortion theoretical support.
citing papers explorer
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GenTS: A Comprehensive Benchmark Library for Generative Time Series Models
GenTS is a modular benchmark library providing unified data pipelines, generative models, and evaluation metrics for time series synthesis, forecasting, and imputation, with open-source code and initial benchmarking experiments.
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SDFlow: Similarity-Driven Flow Matching for Time Series Generation
SDFlow learns a global transport map via similarity-driven flow matching in VQ latent space, using low-rank manifold decomposition and a categorical posterior to handle discreteness, yielding SOTA long-horizon performance and inference speedups.
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GCGNet: Graph-Consistent Generative Network for Time Series Forecasting with Exogenous Variables
GCGNet uses a variational generator, graph structure aligner, and graph refiner to jointly capture temporal and channel correlations in time series forecasting with exogenous variables, outperforming baselines on 12 real-world datasets.
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Cyclic Adaptive Private Synthesis for Sharing Real-World Data in Education
CAPS provides an iterative differentially private synthesis method that outperforms one-shot baselines on authentic educational real-world data.
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AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineage
AutoReproduce is a multi-agent system using paper lineage to autonomously reproduce AI experiment code, with a new benchmark showing improvements over baselines in fidelity and execution.
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MSDformer: Multi-scale Discrete Transformer For Time Series Generation
MSDformer introduces a multi-scale discrete transformer that tokenizes time series at multiple scales and models them autoregressively in discrete space, claiming superior performance over prior DTM methods with rate-distortion theoretical support.