TY - JOUR A2 - Jiang, Jun AU - Yu, Yufeng AU - Zhu, Yuelong AU - Li, Shijin AU - Wan, Dingsheng PY - 2014 DA - 2014/10/30 TI - Time Series Outlier Detection Based on Sliding Window Prediction SP - 879736 VL - 2014 AB - In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI), which can be calculated by the predicted value and confidence coefficient. The use of PCI as threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis. SN - 1024-123X UR - https://doi.org/10.1155/2014/879736 DO - 10.1155/2014/879736 JF - Mathematical Problems in Engineering PB - Hindawi Publishing Corporation KW - ER -