Abstract
Time series analysis is a fundamental task in various domains, ranging from finance to healthcare and beyond. Traditional methods for time series analysis often require significant manual effort and expertise. PyCaret, a low-code machine learning library, offers a simplified approach to time series analysis, enabling practitioners to build robust models with minimal code. In this paper, we delve into PyCaret’s capabilities for time series analysis, exploring its methods and comparing them with traditional Python packages. Through examples and case studies, we demonstrate how PyCaret streamlines the time series analysis workflow, making it accessible to a broader audience.
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