Time series outlier detection based on sliding window prediction
type: Item
instance of: scholarly article
title: Time series outlier detection based on sliding window prediction
zbMATH Open document ID: 1407.62335
DOI: 10.1155/2014/879736
author: Yufeng Yu
author: Yuelong Zhu
author: Dingsheng Wan
author: Shi-Jin Li
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 62M10
zbMATH DE Number: 7017513
Wikidata QID: Q59070386
describes a project that uses: Rainbow
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/879736
OpenAlex ID: W2055865901
cites work: Q3880088
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description: scientific article; zbMATH DE number 7017513