For this dataset, we examine the application of various AutoML frameworks to a one-dimensional time-series electrocardiogram (ECG) dataset with a sampling rate of 100 Hz. The goal is to explore their utility in addressing regression tasks using such high-frequency data.

Specifically, we aim to analyze how these frameworks perform when predicting continuous variables from the provided time-series signals. By employing multiple AutoML approaches on this dataset, we can assess their effectiveness and efficiency in handling complex time-series patterns while reducing manual feature engineering efforts. This investigation will provide insights into the strengths and limitations of each framework within the context of real-world biomedical signal processing applications.