Creating Synthetic Time Series Data with MOSTLY AI: A Comprehensive Guide

Table of Contents

1. Introduction
2. Preparing the Data
   • 2.1 Subjects and Attributes
   • 2.2 Loading the Data
   • 2.3 Data Settings
3. Building the Data Model
   • 3.1 Connecting Multiple Datasets
   • 3.2 Adjusting the Model Parameters
   • 3.3 Configuring Sequence Lengths
4. Creating the Synthetic Time Series Data
   • 4.1 Training the Model
   • 4.2 Previewing the Generated Data
5. Evaluating the Synthetic Data
   • 5.1 qa Report
   • 5.2 Cross-Validation Tests
6. Utilizing the Synthetic Data
   • 6.1 Collaboration and Download Options
   • 6.2 Validation with Data Visualization Tools
7. Conclusion

Introduction

In this article, we will explore the process of creating synthetic time series data using the MOSTLY AI platform. Synthetic data has become increasingly important for organizations that need to balance data privacy and collaboration. We will walk through the steps involved in preparing the data, building the data model, and generating the synthetic time series data. Additionally, we will evaluate the accuracy of the synthetic data and discuss how it can be effectively utilized for various purposes.

Preparing the Data

Before we can generate synthetic time series data, we need to ensure that the data is properly prepared. This section focuses on the necessary steps to prepare the data for synthetic generation.

2.1 Subjects and Attributes

To protect the privacy of customers, the data should be organized into subject tables containing all the Relevant fields and attributes. By consolidating the data into a single dataset with one row per subject, we can effectively generate synthetic data without compromising privacy.

2.2 Loading the Data

Once the subject tables are prepared, they can be uploaded into the MOSTLY AI platform. This can be done by either dragging and dropping the dataset or browsing for the file directly. The platform allows for easy loading and management of the subject tables.

2.3 Data Settings

In the data settings tab, we can review and configure the columns of the dataset. MOSTLY AI automatically makes initial choices based on the field types. Specific configuration options, such as treating a field as a primary key or a numeric field, can be adjusted to suit the data. These settings play a crucial role in generating accurate synthetic data.

Building the Data Model

Once the data is prepared, we can proceed to build the data model. This section focuses on connecting multiple datasets and adjusting the model parameters to ensure accurate generation of synthetic time series data.

3.1 Connecting Multiple Datasets

In scenarios where multiple datasets are involved, such as linking customers with their behavioral event data, the platform allows for defining connections between the datasets. By specifying the relationships between the tables and configuring the generation formats, the synthetic data can accurately reflect the correlations within the original data.

3.2 Adjusting the Model Parameters

To optimize the synthetic data generation, we can adjust the machine learning parameters and model size. This fine-tuning helps in achieving a more accurate representation of the original data. Additionally, sequence lengths can be adjusted to ensure that the model captures the behavioral Patterns effectively.

3.3 Configuring Sequence Lengths

The length of sequences in the data plays a crucial role in training the model. By defining the sequence length, we can determine the number of records per subject that the model will learn from. The platform offers options for restricting the training to specific sequence lengths or keeping all sequences regardless of their length.

Creating the Synthetic Time Series Data

With the data model built and the parameters configured, we can proceed to create the synthetic time series data. This section guides us through the process of training the model, generating the synthetic data, and previewing the results.

4.1 Training the Model

The model must undergo training to generate the synthetic time series data. The platform handles the training process by utilizing the specified model parameters and sequence lengths. This step is crucial in ensuring the accuracy and coherence of the generated data.

4.2 Previewing the Generated Data

Once the model training is complete, we can preview the synthetically generated data. The platform provides a browser-based preview of sample records from the synthetic dataset. This preview allows us to assess the quality and privacy-safety of the generated data before further utilization.

Evaluating the Synthetic Data

To ensure the accuracy and reliability of the synthetic time series data, it is essential to evaluate its performance. This section focuses on the QA report provided by the platform and cross-validation tests conducted using data visualization tools.

5.1 QA Report

MOSTLY AI provides a QA report that enables us to assess the accuracy and distributions of the synthetic data compared to the original data. By evaluating correlation matrices and other metrics, we gain insights into the fidelity of the synthetic data.

5.2 Cross-Validation Tests

To gain a deeper understanding of the synthetic data's performance, we can perform cross-validation tests using data visualization tools. By comparing the distributions, descriptive statistics, and time series trends of the original and synthetic data, we can validate the accuracy and coherence of the generated data.

Utilizing the Synthetic Data

With the synthetic time series data generated and evaluated, we can now explore its various applications and benefits. This section discusses collaboration options, download formats, and validation with data visualization tools.

6.1 Collaboration and Download Options

The synthetic data can be utilized for collaboration, analytics, and machine learning initiatives. The platform provides options for collaborating with others and sharing the synthetic dataset. Additionally, the synthetic data can be downloaded in CSV or parquet formats for use in various analytical tools.

6.2 Validation with Data Visualization Tools

To further validate the synthetic data, we can utilize data visualization tools outside of the MOSTLY AI platform. By loading the original and synthetic datasets into tools like Jupyter notebooks, we can visually analyze and compare the distributions, statistical measures, and time series trends of both datasets. These validations provide assurance of the accuracy and coherence of the synthetic data.

Conclusion

In this article, we have explored the process of creating synthetic time series data using the MOSTLY AI platform. By following the steps of data preparation, building the data model, generating the synthetic data, and evaluating its accuracy, organizations can achieve privacy-safe and realistic synthetic datasets. The utilization of synthetic data opens up opportunities for collaboration, analytics, and machine learning while preserving data privacy. With the advancements in synthetic data generation, organizations can confidently leverage synthetic data for a wide range of applications.

Highlights

• Synthetic time series data generation made simple with MOSTLY AI platform
• Protecting customer privacy by consolidating data into subject tables
• Configuring data settings and model parameters for accurate generation
• Previewing and evaluating generated synthetic time series data
• Collaborating, downloading, and validating the synthetic data for various applications

FAQ

Q: Can synthetic data perfectly mimic the original data? A: While synthetic data aims to replicate the patterns and statistical measures of the original data, it may not perfectly mimic every aspect. The goal is to achieve a high level of accuracy and coherence while preserving privacy.

Q: How can synthetic data be utilized for machine learning? A: Synthetic data can serve as a privacy-safe alternative for training and testing machine learning models. By using synthetic data, organizations can mitigate risks associated with using sensitive or personal data.

Q: Is synthetic data generation suitable for all industries? A: Synthetic data generation can be utilized across various industries, including healthcare, finance, retail, and more. It offers a way to balance the need for data privacy with the requirements of analytics and machine learning initiatives.

Q: Can synthetic data be used in place of real data for analysis? A: Synthetic data can be used for exploratory analysis and collaboration where real data may not be available or accessible. However, for certain analyses that require the fine-grained details or specific characteristics of the original data, real data may still be necessary.

Q: Are there any limitations or challenges with synthetic data generation? A: Synthetic data generation relies on the quality and representativeness of the original data. If the original data is biased or incomplete, the synthetic data may inherit these limitations. Additionally, protecting privacy and ensuring data compliance are important considerations in the synthetic data generation process.

Resources

• MOSTLY AI - https://www.mostly.ai/