This study evaluates three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—for time series forecasting using diverse datasets, including Bitcoin, COVID-19, and weather data. The results show that the performance of each tool is highly dependent on the specific dataset and its ability to handle time series complexities. AutoGluon, Auto-Sklearn, and PyCaret each have unique strengths and limitations, with AutoGluon showing the best performance on the weather dataset, Auto-Sklearn on the Bitcoin dataset, and PyCaret on the COVID-19 dataset. The study highlights the importance of dataset-specific considerations in time series analysis and provides insights for practitioners and researchers. The findings emphasize the need for further research and development in AutoML for time series forecasting. The study also discusses the limitations of the research, including the rapidly evolving nature of AutoML and the impact of dataset size and characteristics on model performance. Overall, the study provides a comprehensive comparison of AutoML tools for time series forecasting, offering valuable insights for future research and application.This study evaluates three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—for time series forecasting using diverse datasets, including Bitcoin, COVID-19, and weather data. The results show that the performance of each tool is highly dependent on the specific dataset and its ability to handle time series complexities. AutoGluon, Auto-Sklearn, and PyCaret each have unique strengths and limitations, with AutoGluon showing the best performance on the weather dataset, Auto-Sklearn on the Bitcoin dataset, and PyCaret on the COVID-19 dataset. The study highlights the importance of dataset-specific considerations in time series analysis and provides insights for practitioners and researchers. The findings emphasize the need for further research and development in AutoML for time series forecasting. The study also discusses the limitations of the research, including the rapidly evolving nature of AutoML and the impact of dataset size and characteristics on model performance. Overall, the study provides a comprehensive comparison of AutoML tools for time series forecasting, offering valuable insights for future research and application.