Abstract
The article introduces a method for diagnosing the stability of large-scale processes, centered around analyzing the fractal structure of time series. This method comprises several stages, such as data preparation, calculating fractal characteristics, and constructing a stability model based on the acquired data. Application of this method to real data demonstrated its effectiveness in both anomaly detection and predicting process stability.
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