Diffusion Transformers for Tabular Data Time Series Generation
Tabular data generation has recently attracted a growing interest due to its different application scenarios. However, generating time series of tabular data, where each element of the series depends on the others, remains a largely unexplored domain. This gap is probably due to the difficulty of jointly solving different problems, the main of which are the heterogeneity of tabular data (a problem common to non-time-dependent approaches) and the variable length of a time series. In this paper, we propose a Diffusion Transformers (DiTs) based approach for tabular data series generation. Inspired by the recent success of DiTs in image and video generation, we extend this framework to deal with heterogeneous data and variable-length sequences. Using extensive experiments on six datasets, we show that the proposed approach outperforms previous work by a large margin.
Code (1)
Tasks
Tabular Data GenerationTime SeriesTime Series GenerationVideo GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
TimeAutoDiff: Combining Autoencoder and Diffusion model for time series tabular data synthesizing
In this paper, we leverage the power of latent diffusion models to generate synthetic time series tabular data. Along with the temporal and feature correlations, the heterogeneous nature of the feature in the table has b…
DenoisingTime SeriesAttention Augmented Convolutional Transformer for Tabular Time-series
Time-series classification is one of the most frequently performed tasks in industrial data science, and one of the most widely used data representation in the industrial setting is tabular representation. In this work, …
Language ModelingLanguage ModellingQuantizationRepresentation Learning+3Tabular Transformers for Modeling Multivariate Time Series
Tabular datasets are ubiquitous in data science applications. Given their importance, it seems natural to apply state-of-the-art deep learning algorithms in order to fully unlock their potential. Here we propose neural n…
Fraud DetectionSynthetic Data GenerationTime SeriesTime Series AnalysisFine-grained Attention in Hierarchical Transformers for Tabular Time-series
Tabular data is ubiquitous in many real-life systems. In particular, time-dependent tabular data, where rows are chronologically related, is typically used for recording historical events, e.g., financial transactions, h…
Time SeriesExtending Tabular Denoising Diffusion Probabilistic Models for Time-Series Data Generation
Diffusion models are increasingly being utilised to create synthetic tabular and time series data for privacy-preserving augmentation. Tabular Denoising Diffusion Probabilistic Models (TabDDPM) generate high-quality synt…