Continuous Evolution Pool: Taming Recurring Concept Drift in Online Time Series Forecasting
This addresses a prevalent issue in time series analysis for applications requiring real-time predictions, but it is incremental as it builds on existing parameter updating techniques by adding knowledge retention mechanisms.
The paper tackles the problem of recurring concept drift in online time series forecasting, where old data patterns reappear and cause accuracy decline, by proposing the Continuous Evolution Pool (CEP) to store and retrieve forecasters for different concepts, resulting in significantly enhanced prediction results as demonstrated on eight real datasets.
Recurring concept drift, a type of concept drift in which previously observed data patterns reappear after some time, is one of the most prevalent types of concept drift in time series. As time progresses, concept drift occurs and previously encountered concepts are forgotten, thereby leading to a decline in the accuracy of online predictions. Existing solutions employ parameter updating techniques to delay forgetting; however, this may result in the loss of some previously learned knowledge while neglecting the exploration of knowledge retention mechanisms. To retain all conceptual knowledge and fully utilize it when the concepts recur, we propose the Continuous Evolution Pool (CEP), a pooling mechanism that stores different instances of forecasters for different concepts. Our method first selects the forecaster nearest to the test sample and then learns the features from its neighboring samples - a process we refer to as the retrieval. If there are insufficient neighboring samples, it indicates that a new concept has emerged, and a new model will evolve from the current nearest sample to the pool to store the knowledge of the concept. Simultaneously, the elimination mechanism will enable outdated knowledge to be cleared to ensure the prediction effect of the forecasters. Experiments on different architectural models and eight real datasets demonstrate that CEP effectively retains the knowledge of different concepts. In the scenario of online forecasting with recurring concepts, CEP significantly enhances the prediction results.