Temporal based Approach for the Concept Drift Adaptation in Stream Data Processing

Abstract

quotOver the past decades, tremendous technological advancement has massively increased the newlinenumerous applications such as financial market analysis, email systems, weather forecasting, newlineenergy consumption monitoring, fraud detection, web mining, social media monitoring, and newlinereal-time sensor data processing that produce vast amounts of data streams at rapid rates with newlineuninterrupted data flows. One significant challenge is the non-static data stream patterns, where newlinethe underlying relationships and patterns can evolve. This phenomenon, termed concept drift, newlinecan severely impair the performance of previously trained models as they fail to account for newlinethese changes in data distribution. In dynamic environments where data continuously evolves, newlinetraditional static learning models become obsolete over time. In data streams, vast amounts newlineof fast-changing data are processed sequentially, so handling concept drift becomes especially newlinecritical. newlineDifferent methods have been developed for drift detection and adaptation. The traditional newlinemodels assume that an underlying pattern (i.e., the data distribution) and the concepts (i.e., newlinetarget labels and their relationship with input data) never change. A machine learning model newlineconsiders that data never changes, which affects the model s performance. Consequently, be cause previous knowledge of data pattern changes is necessary, traditional machine learning newlinealgorithms struggle to cope with concept drift. Furthermore, deep learning algorithms excel at newlinefinding intricate patterns in large amounts of data. However, when processing data streams, the newlinedeep learning model faces two key issues. First, in streaming data, not all the data is avail able at the time of training. It s difficult to handle a continuously arriving data stream with a newlinestatic neural network structure for faster convergence and drift adaptation. Second, the evolving newlineneural network has to cope with catastrophic forgetting. In catastrophic forgetting, previously newlinegained knowledge is replaced with new information. Performance decre