Introduction synthetic time series data
What is time series synthetic data?
Time series data is a datatype characterized by a sequence of events, observations or measurements collected and ordered with date-time intervals, typically representing changes in a variable over time, and is supported by Syntho.
What are some examples of time series data?
- Financial transactions: payments with credit and / or debit card 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.
What makes synthesizing time series data challenging?
Time series data is more challenging to synthesize because it needs to capture the temporal dependencies and patterns inherent in real-world sequential observations. Unlike independent and identically distributed data, where each observation is unrelated to the others, time series data exhibits dependencies across time steps. Many organizations and most open-source solutions cannot synthesize time series well or do not support time series data at all.
Syntho’s unique approach synthesizes the most complex time series accurately
Our Syntho Engine is optimized to synthesize the most complex time series data accurately. We have optimized our models in collaboration with leading organizations working with the most complex time series data.
We have strategic partnerships with leading organizations
Syntho collaborated with leading organizations, such as the 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.
We 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. This included even complex time series structures, such as time series with:
- Equally spaced intervals
- Un-equally spaced intervals
- Equal length
- Un-equal length
- Univariate time series
- Multivariate time series
Do you have any questions?
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How can I generate synthetic time series data with Syntho?
Our Syntho Engine includes Syntho’s sequence model feature that allows users to synthesize time series data (longitudinal data). When the target data that you would like to synthesize contains time series data, our sequence model will be activated.
Save your synthetic data guide now!
- What is synthetic data?
- Why do organizations use it?
- Value adding synthetic data client cases
- How to start
