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. These tools might not fully support all the complexities and nuances of time-series analysis.
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.
Syntho collaborated with leading organizations, such as Cedars-Sinai Medical Center. These organizations work with the most complex time-series data. This allows Syntho to build the best sequence model being able to synthesize the most complex time series accurately.
Explore the Syntho user documentation
Advance 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
Rare long sequence protection thresholdSyntho 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
Test Data Management
De-Identification & Synthetization
Comprehensive Testing with Representative Date.
Rule-Based Synthetic Data
Simulate Real-World Scenarios.
Subsetting
Create Manageable Date Subsets.
Smart De-Identification
PII Scanner
Identify PII automatically with our AI-powered PII Scanner.
Synthetic Mock Data
Substitute sensitive PII, PHI, and other identifiers.
Consistent Mapping
Preserve referential integrity in an entire relational data ecosystem.
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
Increase the number of data samples in a dataset.
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.
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