###### Abstract  
To obtain high performance, generalization, and accuracy in machine learning applications, such as prediction or anomaly detection, large datasets are a necessary prerequisite. Moreover, the collection of data is time-consuming, difficult, and expensive for many imbalanced or small datasets. These challenges are evident in collecting data for financial and banking services, pharmaceuticals and healthcare, manufacturing and the automobile, robotics car, sensor time-series data, and many more. To overcome the challenges of data collection, researchers in many domains are becoming more and more interested in the development or generation of synthetic data. Generating synthetic time-series data is far more complicated and expensive than generating synthetic tabular data. The primary objective of the paper is to generate multivariate time-series data (for continuous and mixed parameters) that are comparable and evaluated with real multivariate time-series synthetic data. After being trained to produce such data, a novel GAN architecture named as MTS-TGAN is proposed and then assessed using both qualitative measures namely t-SNE, PCA, discriminative and predictive scores as well as quantitative measures, for which an RNN model is implemented, which calculates MAE and MSLE scores for three training phases; Train Real Test Real, Train Real Test Synthetic and Train Synthetic Test Real. The model is able to reduce the overall error up to 13% and 10% in predictive and discriminative scores, respectively. The research's objectives are met, and the outcomes demonstrate that MTS-TGAN is able to pick up on the distribution and underlying knowledge included in the attributes of the real data and it can serve as a starting point for additional research in the respective area. deep neural network; multivariate time-series data; generative adversarial network; synthetic data generation +
Footnote †: journal: Medical  
intelligent (AI) models, developers frequently need huge datasets with precise labeling. As when trained on a wider variety of data, neural networks become more accurate. Collecting and labeling these massive datasets containing hundreds or even millions of objects or information, however, can be unreasonably time and money-consuming, as well as labor-intensive. For these reasons, the generation of synthetic data is crucial. Some areas which have an application of synthetic data are as follows:  
* Financial and banking services. * Pharmaceuticals and healthcare. * Manufacturing and automobiles. * Robotics. * Digital marketing and internet advertising. * Espionage and security businesses. There are different types of data that can be synthesized, such as tabular data, image, video data, sound data, and time-series data. However, creating synthetic time-series data is far trickier than creating synthetic tabular data [3]. The primary distinction is that real tabular data assume that each row of the data contains information on a single person, whereas time-sensitive data, on the other hand, is dispersed across numerous columns and rows in various time sequences or frames. The difficulty of this task is also influenced by the duration of the time-series data; the longer the history, the more challenging it is to learn the features of the original and generated synthetic data from it. Generative adversarial network models (GANs) [4] can supplement smaller datasets by producing new and unused data. In some circumstances, data may be corrupted, distorted, or missing. GANs can impute data, or substitute the anomalies with information corresponding to clean data. In the case of corrupted data, they can also denoise signals. Data security, privacy, and sharing are now severely controlled; they can add an additional degree of protection by creating differentially private datasets that do not run the danger of being linked to their source data [5]. GANs [4] has become a well-liked way for creating or enhancing datasets, particularly with images and videos.