  In the field of machine learning and artificial intelligence, time series
forecasting plays a pivotal role across various domains such as finance,
healthcare, and weather. However, the task of selecting the most suitable
forecasting method for a given dataset is a complex task due to the diversity
of data patterns and characteristics. This research aims to address this
challenge by proposing a comprehensive benchmark for evaluating and ranking
time series forecasting methods across a wide range of datasets. This study
investigates the comparative performance of many methods from two prominent
time series forecasting frameworks, AutoGluon-Timeseries, and sktime to shed
light on their applicability in different real-world scenarios. This research
contributes to the field of time series forecasting by providing a robust
benchmarking methodology and facilitating informed decision-making when
choosing forecasting methods for achieving optimal prediction.
