| 摘要 |
Disclosed is a method and system for detecting outliers in real-time for a univariate time-series signal. The system may receive the univariate time-series signal, comprising a plurality of datasets, from a data source. The system may compute a standard deviation of a dataset of the plurality of datasets. Subsequently, the system may compute the optimal sample block size and the critical sample size of the dataset. Further, the system may determine the optimal operational block size of the dataset. The system may segment the plurality of datasets into blocks based upon the optimal operational block size. The system may detect the outliers by performing an outlier detection technique on the blocks, thereby ensuring improved execution time while minimally affecting precision and accuracy of the outcome of the outlier detection method. |
| 主权项 |
1. A method for detecting outliers in real-time for a univariate time-series signal, the method comprising:
receiving, by a processor 210, a univariate time-series signal from a data source, wherein the univariate time-series signal comprises a plurality of datasets, and wherein each dataset of the plurality of datasets comprises number of univariate time-series data elements; computing, by the processor 210, a standard deviation (σ) of a dataset of the plurality of datasets; computing, by the processor 210, an optimal sample block size () of the dataset by using the standard deviation (σ); computing, by the processor 210, a critical sample size (critical) of the dataset based on the standard deviation (σ), the number of univariate time-series data elements, a predefined accuracy (δ), and a precision (1−ε) of outcome; determining, by the processor 210, an optimal operational block size (operational) of the dataset; segmenting, by the processor 210, the plurality of datasets into blocks based upon the optimal operational block size (operational), wherein each block comprises /operational data elements of the number of univariate time-series data elements; and detecting, by the processor 210, outliers in real-time by performing an outlier detection technique on the blocks. |
