Smart Alerting: A Forecasting and Anomaly Detection System for Data-Driven Decision-Making

In today’s fast-paced and data-intensive environments, organizations require a robust system to communicate forecasts and detect anomalies in real-time. This paper presents a system architecture that integrates forecasting models with an alarm system for anomalies, enabling proactive decision-making and swift response to deviations from expected trends. The proposed system consists of a forecasting module that leverages advanced statistical and machine learning techniques to generate accurate predictions. The forecasting module is coupled with a real-time data processing engine that continuously monitors incoming data streams for anomalies. When an anomaly is detected, the system triggers an alert to stakeholders via OPCUA to the given alert center. The system’s alarm system is designed to categorize anomalies based on their severity and impact, ensuring that stakeholders receive timely and relevant notifications. It also features a customizable notification framework, allowing users to tailor alerts to their specific needs and preferences. The proposed system offers several benefits, including improved decision-making, reduced risk, and enhanced operational efficiency. By providing real-time forecasts and anomaly detection, organizations can respond promptly to changes in their environment, minimizing potential losses and capitalizing on emerging opportunities. The system’s modular design and scalability features make it an attractive solution for various industries, from finance and healthcare to manufacturing and transportation. Overall, the intelligent forecasting and anomaly alert system provides a powerful tool for organizations to stay ahead of the curve and make data-driven decisions in real-time.