Prediction-based Anomaly Detection in Multivariate Time-Series Data

Abstract

Consumer-grade fitness trackers can produce unreliable physiological data due to sensor errors. The same holds for cycling data from Wahoo Fitness, where heart rate (HR) and power readings are essential for training and performance analysis. This thesis presents a prediction-based anomaly detection framework tailored to multivariate time-series cycling data. The approach reframes anomaly detection as a personalized physiological HR prediction problem. We define anomalies as deviations between measured sensor values and their predicted values, based on contextual activity metrics (e.g., power, cadence, speed, altitude, and gradient) and user-specific embeddings. The system combines ordinary differential equations (ODEs) modeling heart rate dynamics with machine learning techniques to capture non-linear, non-stationary, and individualized relationships. The model not only detects implausible values but reconstructs them with physiologically consistent alternatives. Compared to reconstruction-based methods, which are mostly used for anomaly detection in time series data, this physiologically grounded approach better differentiates between normal variation and true anomalies. Experimental results demonstrate effective identification and correction of HR and power anomalies, contributing to improved data quality and reliability in wearable fitness applications.