Creating interpretable synthetic time series for enhancing the design and implementation of Internet of Things (IoT) solutions

Abstract

This study establishes a foundation for addressing the challenge of developing Internet of Things (IoT) solutions in the absence of real-world data, a common obstacle in the early stages of IoT design, prototyping, and testing. Motivated by the need for reliable and interpretable synthetic data, this work introduces a structured approach and a dedicated library for creating realistic time series data. The methodology emphasizes flexibility and modularity, allowing for the combination of distinct components–such as trends, seasonality, and noise–to create synthetic data that accurately reflects real-world phenomena while maintaining interpretability. The approach’s utility is demonstrated by creating synthetic air temperature time series, which are rigorously compared against real-world datasets to assess their fidelity. The results validate the proposed methodology’s and library’s effectiveness in producing data that closely mirrors real-world patterns, providing a robust tool for IoT development in data-constrained environments.