Affiliations: [a] Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology, Ho Chi Minh City, Vietnam | [b] Vietnam National University, Ho Chi Minh City, Vietnam | [c] Department of IT, Ho Chi Minh City University of Foreign Languages and Information Technology, Ho Chi Minh City, Vietnam
Correspondence:
[*]
Corresponding author: Thuy Huynh Thi Thu, Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Ho Chi Minh City, Vietnam. E-mail: [email protected].
Abstract: Time series forecasting has many practical applications in a variety of domains such as commerce, finance, medicine, weather, environment, and transportation. There exist so many methods developed for time series forecasting. However, most of the forecasting methods do not pay attention to anomalies in time series even though time series are sensitive to anomalies. Anomaly patterns cause negative effects on the accuracy of time series forecasting. In this paper, we propose a novel anomaly repair-based approach to improve time series forecasting in the case of anomaly existence. In our approach, an effective time series forecasting framework, EPL_S_X, is proposed with anomaly smoothing as a pre-processing stage and any existing time series prediction algorithm X. In particular, our proposed approach consists of three steps including detecting anomalies, repairing anomalies by using our smoothing method, and forecasting time series using preprocessed time series. Experimental results on several time series datasets reveal that our proposed approach improves remarkably the accuracy of many existing time series forecasting methods. It also outperforms the two robust time series forecasting methods that are based on exponential and Holt-Winters smoothing. With such better prediction performance, our approach is not only more effective but also more useful when dealing with anomalies in time series forecasting.
Keywords: Time series forecasting, anomaly detection, anomaly smoothing, k-nearest neighbors, Holt-Winters, artificial neural network
