Nested sequences for preserving correlations
Synthesize nested sequences to retain correlation and context - Maintain correlations between past and future data points with contextual synthetic data generation.
What are nested sequences?
Nested sequences take data accuracy and quality to the next level, particularly when dealing with linked tables in a standard table relationship schema.
Let's consider a familiar scenario: Customer <- Account <- Transaction. Historically, each transaction within an account was generated independently, lacking any connection to previous transactions or accounts. Our goal is to maintain correlations between prior transactions, enabling the creation of new transactions and accounts with enhanced context.
Let's consider a familiar scenario: Customer <- Account <- Transaction. Historically, each transaction within an account was generated independently, lacking any connection to previous transactions or accounts. Our goal is to maintain correlations between prior transactions, enabling the creation of new transactions and accounts with enhanced context.
While the process remains similar, we've achieved a significant enhancement during the generation of your synthetic dataset: the introduction of previous transactions as context for generating items in future transactions for the same customer.
In other words, for multi-table setups with a 3-level hierarchy, any correlation between the 3rd level entities (Transaction) and all the 2nd level entities (Account), that link to the same subject (Customer), is now retained.
In other words, for multi-table setups with a 3-level hierarchy, any correlation between the 3rd level entities (Transaction) and all the 2nd level entities (Account), that link to the same subject (Customer), is now retained.
In our example of Customer <- Account <- Transaction, all transactions now retain correlations to all other accounts that belong to the same Customer.
Learn more about Nested Sequences in our Documentation.Sign up nowBenefits
Elevate the accuracy and quality of your synthetic dataset by maintaining correlations and relationships between past and future data points.
Improved data quality: the context provided by nested sequences ensures that generated data adheres to logical patterns and relationships.
Reduces the likelihood of errors and inconsistencies in your synthetic dataset.
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