Advancing IoT Data Utilization: Generating and Evaluating Synthetic Time Series Data

Synthetic data generation plays a crucial role in various domains where access to real-world datasets is limited or restricted due to legal, ethical, or privacy concerns. Given the growing need for realistic data in analysis and research, we explore three distinct methods to capture real time series data's temporal and distributional characteristics. We provide a comparative study of these methods by using a set of evaluation metrics, highlighting the strengths and benefits of each approach. This analysis offers multiple perspectives on the utility and applicability of the proposed synthetic data generation techniques. Additionally, we investigate the impact of synthetic data when forecasting time series, shedding light on its potential for enhancing predictive models.