Description: Bump detection is a crucial process in data analysis that focuses on identifying sudden or unusual changes in data sets. These changes, known as ‘bumps’, can be indicative of anomalies that require attention, as they may signal underlying problems or significant events. This process relies on statistical techniques and artificial intelligence (AI) that enable systems to learn normal patterns in data and, from there, detect deviations that may not be immediately apparent. Bump detection is particularly relevant in contexts where data integrity is critical, such as in industrial system monitoring, financial fraud detection, or health data analysis. By identifying these changes, organizations can act quickly to investigate and mitigate potential risks, thereby optimizing decision-making and improving operational efficiency. In a world where data is becoming increasingly abundant, the ability to effectively detect anomalies has become an essential tool for businesses and researchers.
