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Anomaly detection is crucial in the petroleum industry for monitoring critical machinery. [20] Martí et al. used a novel segmentation algorithm to analyze sensor data for real-time anomaly detection. [20] This approach helps promptly identify and address any irregularities in sensor readings, ensuring the reliability and safety of petroleum ...
In statistical analysis, change detection or change point detection tries to identify times when the probability distribution of a stochastic process or time series changes. In general the problem concerns both detecting whether or not a change has occurred, or whether several changes might have occurred, and identifying the times of any such ...
Density estimation is also frequently used in anomaly detection or novelty detection: [8] if an observation lies in a very low-density region, it is likely to be an anomaly or a novelty. In hydrology the histogram and estimated density function of rainfall and river discharge data, analysed with a probability distribution , are used to gain ...
Time series: random data plus trend, with best-fit line and different applied filters. In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time.
Whereas recursive neural networks operate on any hierarchical structure, combining child representations into parent representations, recurrent neural networks operate on the linear progression of time, combining the previous time step and a hidden representation into the representation for the current time step. From a time-series perspective ...
Isolation Forest is an algorithm for data anomaly detection using binary trees.It was developed by Fei Tony Liu in 2008. [1] It has a linear time complexity and a low memory use, which works well for high-volume data.
Autoencoders are applied to many problems, including facial recognition, [5] feature detection, [6] anomaly detection, and learning the meaning of words. [7] [8] In terms of data synthesis, autoencoders can also be used to randomly generate new data that is similar to the input (training) data. [6]
Time-series analysis of time-dependent data. [5] Clustering and classification to find patterns and associations among groups of data. [5] Data matching Data matching is used to compare two sets of collected data. The process can be performed based on algorithms or programmed loops.