Sequential RC-TGAN: Generating Relational Time Series with Spectral Envelope Loss

• URL: http://arxiv.org/abs/2606.31904v1
• Date: Tue, 30 Jun 2026 16:09:10 GMT
• Title: Sequential RC-TGAN: Generating Relational Time Series with Spectral Envelope Loss
• Abstract summary: We introduce Sequential RC-TGAN (Seq. RC-TGAN), a temporal extension of the RC-TGAN framework, equipped with a novel integrated loss function.<n>Our end-to-end approach significantly outperforms state-of-the-art systems in reproducing cyclic patterns and long-term seasonality.
• Score: 2.6599014990168834
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The generation of synthetic relational databases often involves modeling complex temporal dynamics, such as transaction logs or event sequences. A significant challenge in this domain is the handling of categorical time series (e.g., status codes), where standard encoding methods like one-hot encoding fail to capture intrinsic frequency-domain features such as seasonality and cyclicity. In this paper, we introduce Sequential RC-TGAN (Seq. RC-TGAN), a temporal extension of the RC-TGAN framework, equipped with a novel integrated loss function based on the \textit{Spectral Envelope Theory}. This differentiable loss allows the generator to directly optimize the preservation of latent periodic structures via backpropagation. While spectral envelope theory is inherently designed for categorical sequences, we extend this frequency-domain regularization to continuous time series by employing a Variational Gaussian Mixture Model (VGM) discretization strategy. To establish a mathematically rigorous evaluation standard, we simulate categorical time series governed by a parameter $α$, with exactly known theoretical spectral envelopes. Integrating these dynamic sequences into the child tables of a relational database yields a robust ground-truth benchmark for evaluating the frequency-domain fidelity of our generative framework. Furthermore, we address the lack of robust evaluation standards for relational time series by proposing two new metrics: Spectral Density Divergence and Spectral Envelope Divergence. Experimental results on real-world datasets, as well as our simulated benchmarks, demonstrate that our end-to-end approach significantly outperforms state-of-the-art systems in reproducing cyclic patterns and long-term seasonality across both categorical and continuous features.

Related papers

• REGEN: Reference-Guided Synthetic Multivariate Time Series Generation for Forecasting [7.771378647684901]
  We propose a reference-guided generative pipeline that treats observed sequences as structural scaffolds for synthesis.<n>We show that ReGeN-generated data consistently substitutes for real sibling data with minimal forecasting degradation.<n>This suggests that in low-data regimes, how reference data is structurally exploited can matter as much as how much data is available.
  arXiv  Detail & Related papers  (2026-06-03T16:19:55Z)
• Generative Modeling of Approximately Periodic Time Series by a Posterior-Weighted Gaussian Process [0.4460583138505672]
  We propose a generative model for approximately periodic time series.<n>Our approach decouples intra-repetition structure from inter-repetition variability through a two-stage construction.
  arXiv  Detail & Related papers  (2026-05-13T08:17:00Z)
• Preserving Temporal Dynamics in Time Series Generation [0.2099922236065961]
  Time-series data augmentation plays a crucial role in regression-oriented forecasting tasks.<n>Existing approaches primarily focus on matching marginal data distributions.<n>We propose a model-agnostic Markov Chain Monte Carlo framework to mitigate distribution shift and preserve temporal dynamics.
  arXiv  Detail & Related papers  (2026-04-29T20:32:45Z)
• Forecasting as Rendering: A 2D Gaussian Splatting Framework for Time Series Forecasting [79.37674445572462]
  Time series forecasting (TSF) remains a challenging problem due to the intricate entanglement of intraperiod-fluctuations and interperiod-trends.<n>Treating reshaped tensors as static images results in a topological mismatch.<n> relying on uniform fixed-size representations allocates modeling capacity inefficiently.<n>TimeGS is a novel framework that fundamentally shifts the forecasting paradigm from regression to 2D generative rendering.
  arXiv  Detail & Related papers  (2026-02-10T14:13:36Z)
• Contextual and Seasonal LSTMs for Time Series Anomaly Detection [49.50689313712684]
  We propose a novel prediction-based framework named Contextual and Seasonal LSTMs (CS-LSTMs)<n>CS-LSTMs are built upon a noise decomposition strategy and jointly leverage contextual dependencies and seasonal patterns.<n>They consistently outperform state-of-the-art methods, highlighting their effectiveness and practical value in robust time series anomaly detection.
  arXiv  Detail & Related papers  (2026-02-10T11:46:15Z)
• A Unified Frequency Domain Decomposition Framework for Interpretable and Robust Time Series Forecasting [81.73338008264115]
  Current approaches for time series forecasting, whether in the time or frequency domain, predominantly use deep learning models based on linear layers or transformers.<n>We propose FIRE, a unified frequency domain decomposition framework that provides a mathematical abstraction for diverse types of time series.<n>Fire consistently outperforms state-of-the-art models on long-term forecasting benchmarks.
  arXiv  Detail & Related papers  (2025-10-11T09:59:25Z)
• TIMED: Adversarial and Autoregressive Refinement of Diffusion-Based Time Series Generation [0.31498833540989407]
  TIMED is a unified generative framework that captures global structure via a forward-reverse diffusion process.<n>To further align the real and synthetic distributions in feature space, TIMED incorporates a Maximum Mean Discrepancy (MMD) loss.<n>We show that TIMED generates more realistic and temporally coherent sequences than state-of-the-art generative models.
  arXiv  Detail & Related papers  (2025-09-23T23:05:40Z)
• Multivariate Long-term Time Series Forecasting with Fourier Neural Filter [42.60778405812048]
  We introduce FNF as the backbone and DBD as architecture to provide excellent learning capabilities and optimal learning pathways for spatial-temporal modeling.<n>We show that FNF unifies local time-domain and global frequency-domain information processing within a single backbone that extends naturally to spatial modeling.
  arXiv  Detail & Related papers  (2025-06-10T18:40:20Z)
• MFRS: A Multi-Frequency Reference Series Approach to Scalable and Accurate Time-Series Forecasting [51.94256702463408]
  Time series predictability is derived from periodic characteristics at different frequencies.<n>We propose a novel time series forecasting method based on multi-frequency reference series correlation analysis.<n> Experiments on major open and synthetic datasets show state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-11T11:40:14Z)
• Causal Temporal Regime Structure Learning [49.77103348208835]
  We present CASTOR, a novel method that concurrently learns the Directed Acyclic Graph (DAG) for each regime.<n>We establish the identifiability of the regimes and DAGs within our framework.<n>Experiments show that CASTOR consistently outperforms existing causal discovery models.
  arXiv  Detail & Related papers  (2023-11-02T17:26:49Z)
• Stochastically forced ensemble dynamic mode decomposition for forecasting and analysis of near-periodic systems [65.44033635330604]
  We introduce a novel load forecasting method in which observed dynamics are modeled as a forced linear system. We show that its use of intrinsic linear dynamics offers a number of desirable properties in terms of interpretability and parsimony. Results are presented for a test case using load data from an electrical grid.
  arXiv  Detail & Related papers  (2020-10-08T20:25:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.