Synthesize time-series data accurately with Syntho
Time-series data is more challenging to synthesize. Unlike regular tabular data, where each row represents an independent observation, time-series data contains cross-row dependencies, in which each row represents a subsequent observation.
There are various open-source packages available for handling time-series data, but their quality can often be suboptimal.
With our Syntho Engine, you can accurately synthesize data containing time series. Our approach adeptly captures correlations and statistical patterns between the entity table and the associated table containing longitudinal information.
Explore the Syntho user documentation
Advanced modeling techniques
Syntho utilizes state-of-the-art AI and machine learning algorithms specifically designed to capture the unique patterns and dependencies in time-series data, ensuring realistic and high-fidelity synthetic datasets.
Rare long sequence protection threshold
Syntho offers advanced settings to limit the maximum sequence length used during training, preventing outliers with unusually long sequences from being identifiable.
Sequence model configuration
Syntho provides configurable parameters for sequence modeling, such as maximum sequence length and rare long sequence protection, to manage computational resources efficiently and enhance privacy.
Batch processing and sampling
Syntho optimizes data generation by allowing users to define batch sizes and select random samples for training, balancing between performance and data representativeness.
Statistical integrity
Regularly validate that the synthetic time-series data maintains the statistical properties of the original data, such as mean, variance, and autocorrelation, ensuring it is representative of real-world scenarios.
Create a workspace consisting of a source and a destination database.
Set preprocessing, table settings, PII scanning, and advanced generator options.
Begin generating, and the time-series data process will be complete.
Explore other features that we provide
Data Masking
PII Scanner
Identify PII automatically with our AI-powered PII Scanner.
Synthetic Mock Data
Simulate Real-World Scenarios.
Consistent Mapping
Preserve referential integrity in an entire relational data ecosystem.
Rule-Based Synthetic Data
Formula-Based Synthetic Data
Generate Synthetic Data according to defined formulas
Pattern-Based Synthetic Data
Generate Synthetic Data according to patterns
Subsetting
Increase the number of data samples in a dataset.
AI Generated Synthetic Data
Quality Assurance Report
Assess generated synthetic data on accuracy, privacy, and speed.
Time Series Synthetic Data
Synthesize time-series data accurately with Syntho.
Upsampling
Create Manageable Date Subsets.
Time series data is a datatype characterized by a sequence of events, observations, or measurements collected and ordered with time intervals, typically representing changes in a variable over time, and is supported by Syntho.
Explore with us how to create data that mimics real data, safely and efficiently, using synthetic data
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