## 4 Methodology
### Proposed Model and Synthetic Data Generation
Six stacked GRUs [15], each with 32 units, were used to construct the generator, discriminator, embedder, and recovery function networks in the proposed model. It is possible for a network to learn deeper and more complicated patterns by using stacked GRUs, which is unquestionably the case with six signals, respectively, with various features. Trial and error led to the decision to use six layered GRUs with 32 units on one, two, and three GRU layers followed by 16 units on four, five and six layers. The generated sequences/samples are then subjected to division into sets for training, testing, and validation. Of the data, 10% is in the test set, 15% is in the validation set, and training is performed with the remaining 75% of the data. Both the generated and the real data samples are subjected to this splitting individually. Now, utilizing datasets based on Train Real Test Synthetic (TRTS), Train Real Test Real (TRTR), and Train Synthetic Test Real (TSTR), the RNN model is implemented for 400 iterations and tested which is explained in the next subsection. Here, we discuss the training part of the model, but before using the latent codes of the real samples to train the entire model, including adversarial and auto-encoding components together, it was first necessary to pre-train the auto-encoding components to make sure they were relatively representative. One hundred epochs of pre-training were completed, with each epoch denoting a network update following a full run over the training dataset. In the pre and main training, samples from the training dataset were taken from the entire dataset for each epoch. Figure 4: Components of MTS-TGAN model. Figure 5: The adversarial sub-components of MTS-TGAN: (**a**) generator (**b**) discriminator and the auto-encoding sub-components of MTS-TGAN: (**c**) embedder (**d**) recovery functions, respectively. For convenience and to quickly identify the model with the greatest performance, models were saved for every 1000 epochs during the 50,000 epoch primary training. It was vital to take into account that the best model is not usually the one that has the longest training time. The models would not have been accessible for testing and assessment if they had not been preserved during the training. The generator was trained twice for each epoch to prevent the discriminator from taking over, and a time limit was set for the discriminator's weight updates. The TimeGAN [8] threshold, which indicated that weights were not changed if the discriminator loss was less than 0.15, was the basis for this. Table 4 explains the overall training settings used in the implementation of the model, including parameters from the sequence length to loss function. Unfortunately, there is no consensus on the metrics for evaluating the generated data for time-series GANs [38] because of the very small number of research that has been published so far. Different strategies have been put forth, but as of now, none has emerged as the leader in the metrics field, and the same applies to the situation of time-series (TS) data. However, we evaluated MTS-TGAN both on qualitative and quantitative metrics, which are described in the next subsection.