## 1 Introduction  
In 2006, [PERSON] coined the slogan 'Data are the new Oil' ([[https://futurescot.com/why-data-is-the-new-oil/](https://futurescot.com/why-data-is-the-new-oil/)]([https://futurescot.com/why-data-is-the-new-oil/](https://futurescot.com/why-data-is-the-new-oil/)) accessed on 4 July 2024), emphasizing data's growing importance. Over time, data have become a critical resource, shaping industries and society. Advances in the digital world have transformed how data are generated, managed, and analysed, fostering new business practices and professional roles in data science and mining [1, 2]; however, as noted by [PERSON], the ethical governance and equitable application of data remain essential [3].  
The evolution of data management began with relational databases in the 1980s, progressing to data warehouses (DWHs) in the 1990s, which aggregate and analyse large volumes of data, supporting strategic business decisions through Business Intelligence (BI). Schemas like star (denormalised for speed) and snowflake (normalised for reduced redundancy) optimise these analyses [4].  
The emergence of Big Data, characterised by volume, velocity, variety, veracity, and variability, has further revolutionised data management; similarly, technologies such as the Hadoop, Apache Spark, and NoSQL databases are essential for handling Big Data, addressing challenges such as data quality, scarcity, privacy concerns, and ethical implications [5].  
One promising solution to these challenges is synthetic data, which are generated using algorithms to simulate real datasets and mitigate scarcity, privacy, and class imbalance issues, with applications spanning finance, healthcare, artificial intelligence (AI), natural language processing, and education. It enables broader analyses and facilitates AI, computer vision, and speech synthesis advancements without compromising data security [6].  
Despite these advancements, the generation of high-quality synthetic data remains an open challenge, particularly in maintaining fidelity to real-world data while ensuring diversity and robustness. This work addresses these challenges by proposing novel methodologies that enhance synthetic data generation through advanced modelling techniques. Specifically, the key contributions of this work are as follows:  
* Enhanced Temporal Modelling: The Gated Recurrent Unit (GRU)-based architecture effectively captures long-term dependencies, enabling more accurate synthetic data generation compared to convolutional approaches.
* Improved Latent Space Sampling: Markov Chain Monte Carlo (MCMC) sampling riches latent space exploration, ensuring variability and reducing divergence between real and synthetic data.
* Robustness and Scalability: The model demonstrates resilience to missing data and scalability in data augmentation tasks, maintaining performance across diverse conditions.  
These contributions advance the understanding of synthetic data generation, addressing critical gaps in scalability, robustness, and representational fidelity.  
The remainder of this article is structured as follows. Section 2 reviews the state of the art, while Section 3 provides the theoretical background. Section 4 discusses the model, while the experiments are presented in Sections 5 and 6, respectively. A general discussion reviews the strengths and the limitations of this article in Section 7. Finally, Section 8 concludes the work, outlining directions for future research.