A Novel Anomaly Forecasting in Time‐Series Data: Feedback Connection between Forecasting and Detecting Algorithms with Applications to Power Systems
This article is concerned with developing a novel structure of machine learning‐based anomaly forecasting, by which both forecasting the future states and detecting the anomalies in these states can be achieved at the same time. The main idea of this article is to introduce a feedback connection to...
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| Format: | Article |
| Language: | English |
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Wiley
2025-05-01
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| Series: | Advanced Intelligent Systems |
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| Online Access: | https://doi.org/10.1002/aisy.202401141 |
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| author | Hyung Tae Choi Hae Yeon Park Taewan Kim Jung Hoon Kim |
| author_facet | Hyung Tae Choi Hae Yeon Park Taewan Kim Jung Hoon Kim |
| author_sort | Hyung Tae Choi |
| collection | DOAJ |
| description | This article is concerned with developing a novel structure of machine learning‐based anomaly forecasting, by which both forecasting the future states and detecting the anomalies in these states can be achieved at the same time. The main idea of this article is to introduce a feedback connection to combine several algorithms with respect to the forecasting and the detecting in a single algorithm. More precisely, Xgboost and long short‐term memory are used for forecastor and one‐class support vector machine and robust random cut forest are used for detector. Combining those 2 × 2 schemes leads to the overall four algorithms, and future anomalies can be detected before they occur. The effectiveness of the proposed algorithms is verified through some comparative simulations of an IEEE 3‐bus system with various faults. More interestingly, the detecting accuracies obtained through the two schemes of taking robust random cut forest are shown to be improved by 10% than those of employing the one‐class support vector machine. For the forecasting part, Xgboost is regarded as involving the fastest prediction speed for online implementations, and thus the combination of Xgboost and robust random cut forest can be the most suitable choice for anomaly forecasting for power system fault events. |
| format | Article |
| id | doaj-art-ea67741e90ff421aaa94ed83daa1ce78 |
| institution | Kabale University |
| issn | 2640-4567 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advanced Intelligent Systems |
| spelling | doaj-art-ea67741e90ff421aaa94ed83daa1ce782025-08-20T03:47:44ZengWileyAdvanced Intelligent Systems2640-45672025-05-0175n/an/a10.1002/aisy.202401141A Novel Anomaly Forecasting in Time‐Series Data: Feedback Connection between Forecasting and Detecting Algorithms with Applications to Power SystemsHyung Tae Choi0Hae Yeon Park1Taewan Kim2Jung Hoon Kim3Department of Electrical Engineering Pohang University of Science and Technology (POSTECH) Pohang 37673 Republic of KoreaDepartment of Electrical Engineering Pohang University of Science and Technology (POSTECH) Pohang 37673 Republic of KoreaDepartment of Electrical Engineering Pohang University of Science and Technology (POSTECH) Pohang 37673 Republic of KoreaDepartment of Electrical Engineering Pohang University of Science and Technology (POSTECH) Pohang 37673 Republic of KoreaThis article is concerned with developing a novel structure of machine learning‐based anomaly forecasting, by which both forecasting the future states and detecting the anomalies in these states can be achieved at the same time. The main idea of this article is to introduce a feedback connection to combine several algorithms with respect to the forecasting and the detecting in a single algorithm. More precisely, Xgboost and long short‐term memory are used for forecastor and one‐class support vector machine and robust random cut forest are used for detector. Combining those 2 × 2 schemes leads to the overall four algorithms, and future anomalies can be detected before they occur. The effectiveness of the proposed algorithms is verified through some comparative simulations of an IEEE 3‐bus system with various faults. More interestingly, the detecting accuracies obtained through the two schemes of taking robust random cut forest are shown to be improved by 10% than those of employing the one‐class support vector machine. For the forecasting part, Xgboost is regarded as involving the fastest prediction speed for online implementations, and thus the combination of Xgboost and robust random cut forest can be the most suitable choice for anomaly forecasting for power system fault events.https://doi.org/10.1002/aisy.202401141anomaly detectionanomaly forecastingfeedback connectionforecastingmachine learning |
| spellingShingle | Hyung Tae Choi Hae Yeon Park Taewan Kim Jung Hoon Kim A Novel Anomaly Forecasting in Time‐Series Data: Feedback Connection between Forecasting and Detecting Algorithms with Applications to Power Systems Advanced Intelligent Systems anomaly detection anomaly forecasting feedback connection forecasting machine learning |
| title | A Novel Anomaly Forecasting in Time‐Series Data: Feedback Connection between Forecasting and Detecting Algorithms with Applications to Power Systems |
| title_full | A Novel Anomaly Forecasting in Time‐Series Data: Feedback Connection between Forecasting and Detecting Algorithms with Applications to Power Systems |
| title_fullStr | A Novel Anomaly Forecasting in Time‐Series Data: Feedback Connection between Forecasting and Detecting Algorithms with Applications to Power Systems |
| title_full_unstemmed | A Novel Anomaly Forecasting in Time‐Series Data: Feedback Connection between Forecasting and Detecting Algorithms with Applications to Power Systems |
| title_short | A Novel Anomaly Forecasting in Time‐Series Data: Feedback Connection between Forecasting and Detecting Algorithms with Applications to Power Systems |
| title_sort | novel anomaly forecasting in time series data feedback connection between forecasting and detecting algorithms with applications to power systems |
| topic | anomaly detection anomaly forecasting feedback connection forecasting machine learning |
| url | https://doi.org/10.1002/aisy.202401141 |
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