An Anomaly Detection Algorithm Selection Service for IoT Stream Data Based on Tsfresh Tool and Genetic Algorithm

Table 1

Summary of stream data anomaly detection algorithms.

Typical algorithms	Category	Characteristic and limitations
Prediction confidence interval (PCI) for time-series outlier detection, simple exponential smoothing (SES) [19], and ARIMA model [20]	Statistical approaches	(1) A supposition about outlier data and normal data need to made first; (2) Domain-specific knowledge is needed for threshold selection depends on
Autoencoder [21], LSTM [22]	Artificial neural computing	Since clustering methods cannot deal with continuous changes in data, therefore careful parameter tuning is needed
Density-based spatial clustering of applications with noise (DBSCAN) [23], subsequence time-series clustering (STSC) [13], isolation forest [24], local outlier factor (LOF) [25], one-class support vector machine (OC-SVM) [26]	Machine learning approaches	Work on stream data; therefore, the normal reference model might be outdated at the moment they are actually used