CatSight, a direct path to proper multi-variate time series change detection: perceiving a concept drift through common spatial pattern

Abstract

Detecting changes in data streams, with the data flowing continuously, is an important problem which Industry 4.0 has to deal with. In industrial monitoring, the data distribution may vary after a change in the machine’s operating point; this situation is known as concept drift, and it is key to detecting this change. One drawback of conventional machine learning algorithms is that they are usually static, trained offline, and require monitoring at the input level. A change in the distribution of data, in the relationship between the input and the output data, would result in the deterioration of the predictive performance of the models due to the lack of an ability to generalize the model to new concepts. Drift detecting methods emerge as a solution to identify the concept drift in the data. This paper proposes a new approach for concept drift detection—a novel approach to deal with sudden or abrupt drift, the most common drift found in industrial processes-, called CatSight. Briefly, this method is composed of two steps: (i) Use of Common Spatial Patterns (a statistical approach to deal with data streaming, closely related to Principal Component Analysis) to maximize the difference between two different distributions of a multivariate temporal data, and (ii) Machine Learning conventional algorithms to detect whether a change in the data flow has been occurred or not. The performance of the CatSight method, has been evaluated on a real use case, training six state of the art Machine Learning (ML) classifiers; obtained results indicate how adequate the new approach is.

A Novel Framework for Concept Drift Detection for Classification Problems in Data Streams

“…The reason behind this performance degradation is related to the unique characteristics of the data (Wares et al, 2019). Speci cally, in real world machine learning scenarios, data characteristics and distribution change over time leading to a non-stationary environment which poses challenges for classi cation models to predict accurately and there is a need to constantly monitor the data stream for detecting such changes and then adapt and retrain the model (Flórez et al, 2023). This phenomenon where the data characteristics and distribution change resulting in a need to update the model is called concept drift and the adaption of the model to the new changes is called concept drift adaptation (Gama et al 2004;Schröder and Schulz 2022).…”