REVIEW 4 cited by
Sequential Models in the Synthetic Data Vault
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
The goal of this paper is to describe a system for generating synthetic sequential data within the Synthetic data vault. To achieve this, we present the Sequential model currently in SDV, an end-to-end framework that builds a generative model for multi-sequence, real-world data. This includes a novel neural network-based machine learning model, conditional probabilistic auto-regressive (CPAR) model. The overall system and the model is available in the open source Synthetic Data Vault (SDV) library {https://github.com/sdv-dev/SDV}, along with a variety of other models for different synthetic data needs. After building the Sequential SDV, we used it to generate synthetic data and compared its quality against an existing, non-sequential generative adversarial network based model called CTGAN. To compare the sequential synthetic data against its real counterpart, we invented a new metric called Multi-Sequence Aggregate Similarity (MSAS). We used it to conclude that our Sequential SDV model learns higher level patterns than non-sequential models without any trade-offs in synthetic data quality.
Forward citations
Cited by 4 Pith papers
-
Seq2Synth: Benchmarking Temporal Fidelity in Synthetic Sequential Tabular Data
Static-distribution fidelity is a poor proxy for temporal fidelity in synthetic sequential tabular data; measuring timestamp, trajectory, cross-sectional, and relational structure over time changes model rankings.
-
Generation of synthetic gait data: application to multiple sclerosis patients' gait patterns
SynGait generates synthetic hip-rotation gait data from small quaternion time-series datasets, reporting good fidelity and geometry preservation on 27 multiple sclerosis patients.
-
AI-Generated Fall Data: Assessing LLMs and Diffusion Model for Wearable Fall Detection
Synthetic fall data from LLMs helps LSTM fall detectors on low-frequency waist datasets (UMAFall +56.83%) but hurts on high-frequency or wrist datasets; diffusion data matches real data best yet does not reliably impr...
-
Resampling Methods that Generate Time Series Data to Enable Sensitivity and Model Analysis in Energy Modeling
Two non-parametric bootstrap schemes plus two displacement methods can produce synthetic energy time series that resemble the original series statistically, but the validity claim rests on descriptive statistics and t...
Discussion (0). Continue with ORCID to comment.