Generative models are being used in all domains. While primarily built for processing texts and images, its reach has been further extended towards data-driven forecasting. Whereas there are many statistical, machine learning and deep learning models for predictive forecasting, generative models are special because they do not need to be trained beforehand saving time and computational power. Also, multivatiate forecasting with the existing models are difficult when the future horizons are unknown for the regressors because they add mode uncertainities in the forecasting process. Thus this study experiments with TimeGPT(Zeroshot) by Nixtla where it tries to identify if the generative model can beat other models like ARIMA, Prophet, NeuralProphet, Linear Regression, XGBoost, Random Forest, LSTM, RNN etc. To asses this, the research created synthetic datasets and synthetic signals to asses the individual model performances and regressor performances for 12 models. The results then used the findings to asses performances of TimeGPT in comparison to the best fitting models in different real world scenarios, The results showed that TimeGPT outperforms multivariate forecasting for weekly granularities by automatically selecting important regressors where as its performance for daily and monthly granularities are still weak.