Online Forecasting and Anomaly Detection Based on the ARIMA Model
Source abstract
Abstract
Real-time diagnostics of complex technical systems such as power plants are critical to keep the system in its working state. An ideal diagnostic system must detect any fault in advance and predict the future state of the technical system, so predictive algorithms are used in the diagnostics. This paper proposes a novel, computationally simple algorithm based on the Auto-Regressive Integrated Moving Average model to solve anomaly detection and forecasting problems. The good performance of the proposed algorithm was confirmed in numerous numerical experiments for both anomaly detection and forecasting problems. Moreover, a description of the Autoregressive Integrated Moving Average Fault Detection (ARIMAFD) library, which includes the proposed algorithms, is provided in this paper. The developed algorithm proves to be an efficient algorithm and can be applied to problems related to anomaly detection and technological parameter forecasting in real diagnostic systems.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fault Detection and Control Systems · Anomaly Detection Techniques and Applications · Risk and Safety Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Skolkovo Institute of Science and Technology, Moscow 143026, Russia
References (23)
Show source-provided references
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