A Methodology for Validating Diversity in Synthetic Time Series Generation.

Area of Science:

• Data Science
• Machine Learning
• Statistical Modeling

Background:

• Robust evaluation of time series models necessitates large datasets, which are often unavailable to researchers.
• Existing synthetic datasets may lack diversity, potentially biasing model evaluation and hindering the development of new predictive methods.

Purpose of the Study:

• To introduce a novel approach for generating and evaluating a high volume of synthetic time series data.
• To address the barrier of data scarcity in time series model evaluation.
• To ensure synthetic datasets capture a broad range of time series characteristics.

Main Methods:

• Development of a construction algorithm for generating synthetic time series data.
• Implementation of a validation framework to evaluate the generated dataset.
• Analysis of the diversity within the synthetic dataset using defined metrics.

Main Results:

• A new method for creating numerous synthetic time series datasets has been presented.
• The construction algorithm and validation framework are detailed.
• An analysis confirms the diversity of the synthetic data, ensuring a broader representation of time series characteristics.

Conclusions:

• The proposed approach facilitates more rigorous and unbiased evaluations of time series models.
• This method overcomes data limitations, enabling deeper analysis and development in the field.
• The generated synthetic datasets offer a valuable resource for researchers in time series analysis.