Why synthesizing time-series data is challenging
Difficult to synthesize accurately
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.
Open-source solutions poorly support time-series data
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.
Support complex time-series data
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.
Strategic partnerships with leading organizations
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.
User documentation
Explore the Syntho user documentation
How do we create high-quality
complex time series data?
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 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.
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.
Time-Series Synthetic Data
Generate more data in 3 steps
Setup a workspace
Create a workspace consisting of a source and a destination database.
Configure data generation parameters
Set preprocessing, table settings, PII scanning, and advanced generator options.
Start generating
Begin generating, and the time-series data process will be complete.
Other features from Syntho
Explore other features that we provide
Test Data Management
Smart De-Identification
Trusted by enterprise companies
Mimic (sensitive) data with AI to generate synthetic data twins
Frequently Asked Questions
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.
- Financial transactions: payments with credit and/or debit cards for transaction monitoring
- Health metrics: heart rate, blood values, cholesterol level
- Energy consumption: smart meter data, electricity usage
- Sensor readings: time-stamped measurements from sensors, such as temperature, flow, etc.
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