Machine learning syntax

Category

Function

Description

Time series

Smooth function

ts_smooth_simple

Uses the Holt Winters algorithm to smooth time series data.

ts_smooth_fir

Uses the finite impulse response (FIR) filter to smooth time series data.

ts_smooth_iir

Uses the infinite impulse response (IIR) filter to smooth time series data.

Multi-period estimation function

ts_period_detect

Estimates time series data by period.

Change point detection function

ts_cp_detect

Detects intervals with differing statistical features, identifying the interval endpoints as change points.

ts_breakout_detect

Detects the points in time at which data experiences dramatic changes.

Maximum value detection function

ts_find_peaks

Detects the local maximum value of time series data in a specified window.

Prediction and anomaly detection function

ts_predicate_simple

Uses default parameters to model time series data, predict time series data, and detect anomalies.

ts_predicate_ar

Uses an autoregressive (AR) model to model time series data, predict time series data, and detect anomalies.

ts_predicate_arma

Uses an autoregressive moving average (ARMA) model to model time series data, predict time series data, and detect anomalies.

ts_predicate_arima

Uses an autoregressive integrated moving average (ARIMA) model to model time series data, predict time series data, and detect anomalies.

ts_regression_predict

Predicts the long-run trend for a single periodic time series.

Sequence decomposition function

ts_decompose

Uses the Seasonal and Trend decomposition using Loess (STL) algorithm to decompose time series data.

Time series clustering function

ts_density_cluster

Uses a density-based clustering method to cluster multiple time series.

ts_hierarchical_cluster

Uses a hierarchical clustering method to cluster multiple time series.

ts_similar_instance

Queries time series curves similar to a specified time series curve.

Kernal density estimation functions

kernel_density_estimation

Fits observed data points using a smooth peak function to simulate the actual probability distribution curve.

Time series padding function

series_padding

Pads data points missing in a time series.

Anomaly comparison function

anomaly_compare

Compares the degree of difference of an observed object in two periods of time.

Pattern mining

Frequent pattern statistical function

pattern_stat

Mines representative combinations of attributes among the given multi-attribute field samples to obtain frequent statistical patterns.

Differential pattern statistical function

pattern_diff

Identifies the pattern that causes differences between two collections in specified conditions.

Root cause analysis function

rca_kpi_search

Analyzes the subdimension attributes that cause anomalies of the monitoring metric.

Correlation analysis functions

ts_association_analysis

Identifies the metrics correlated to a specified metric among multiple observed metrics in the system.

ts_similar

Identifies the metrics correlated to specified time series data among multiple observed ones in the system.

Request URL classification function

url_classify

Classifies a request URL and assigns a tag to it, along with a regular expression that defines the tag's pattern.