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Digital transformation in the public sector provides digital services to the citizens aiming at increasing their quality of life, as well as the transparency and accountability of a public administration. Since adaptation to the citizens changing needs is central for its success, Agile methodologies seem best suited for the software development of digital services in that area. However, as well documented by an attempt to use Scrum for an important Public Administration in Italy, substantial modifications to standard Agile were needed, giving rise to a new proposal called improved Agile (in short, iAgile). Another notable example is the Scrum@IMI method developed by the City of Barcelona for the deployment of its digital services. However, given the importance of digital transformation in the public sector and the scarcity of efforts (documented in the scholarly literature) to effectively bring Agile within it, a strategically important contribution that Computer Science can offer is a general paradigm describing how to tailor Agile methodologies and, in particular, Scrum, for such a specific context. Our proposal, called Scrum@PA, addresses this strategic need. Based on it, a public administration has a technically sound avenue to follow to adopt Scrum rather than a generic set of guidelines as in the current state of the art. We show the validity of our proposal by describing how the quite successful Scrum@IMI approach can be derived from Scrum@PA. Although iAgile can also be derived from our paradigm, we have chosen Scrum@IMI as a pilot example since it is publicly available on GitHub. [ABSTRACT FROM AUTHOR]

Automated Machine Learning (AutoML) tools are revolutionizing the field of machine learning by significantly reducing the need for deep computer science expertise. Designed to make ML more accessible, they enable users to build high-performing models without extensive technical knowledge. This study delves into these tools in the context of time series analysis, which is essential for forecasting future trends from historical data. We evaluate three prominent AutoML tools—AutoGluon, Auto-Sklearn, and PyCaret—across various metrics, employing diverse datasets that include Bitcoin and COVID-19 data. The results reveal that the performance of each tool is highly dependent on the specific dataset and its ability to manage the complexities of time series data. This thorough investigation not only demonstrates the strengths and limitations of each AutoML tool but also highlights the criticality of dataset-specific considerations in time series analysis. Offering valuable insights for both practitioners and researchers, this study emphasizes the ongoing need for research and development in this specialized area. It aims to serve as a reference for organizations dealing with time series datasets and a guiding framework for future academic research in enhancing the application of AutoML tools for time series forecasting and analysis. [ABSTRACT FROM AUTHOR]