Change Point Detection Using Edit Distance

This project explores a novel method for detecting change points in time-series data using Edit Distance, enabling detection in both numerical and textual domains. Traditional change point detection techniques are limited to numerical inputs, but by quantizing data and representing it as symbolic sequences, this approach applies Levenshtein distance to identify structural deviations in system behavior. The algorithm scans for shifts in data patterns using a sliding buffer technique and flags points of semantic change—regardless of magnitude. Validated on diverse datasets such as weather records, eye movement, and stock prices, the method can support real-time monitoring of software systems, data centers, and network traffic to detect faults and anomalies early. This work opens pathways for more adaptive, domain-agnostic fault detection mechanisms in large-scale systems in real-time.