Log Service provides a machine learning feature that supports multiple algorithms and calling methods. You can use SELECT statements and machine learning functions in Search/Analytics to call machine learning algorithms and analyze the characteristics of a field or fields within a period of time.
In particular, Log Service offers various time series analysis algorithms to help you quickly solve problems related to time series prediction, time series anomaly detection, time series decomposition, and multi-time series clustering. In addition, the algorithms are compatible with standard SQL functions. This greatly simplifies the use of the algorithms and improves troubleshooting efficiency.
- Supports various smooth operations on single-time series sequences.
- Supports algorithms related to the prediction, anomaly detection, change point detection, inflexion point detection, and time series forecasting of single-time series sequences.
- Supports decomposition operations on single-time series sequences.
- Supports various clustering algorithms of multi-time series sequences.
- Supports multi-field pattern mining based on the sequence of numeric data or text.
- The input time series data must be sampled from the same interval.
- The input time series data cannot contain data repeatedly sampled from the same time point.
|Valid capacity of time-series data processing||Data can be collected from a maximum of 150,000 consecutive time points.
If this limit is exceeded, you must aggregate the data or reduce the sampling amount.
|Clustering capacity of the density-based clustering algorithm||A maximum of 5,000 time series curves, each of which cannot contain more than 1,440 time points|
|Clustering capacity of the hierarchical clustering algorithm||A maximum of 2,000 time series curves, each of which cannot contain more than 1,440 time points|
Machine learning functions
|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.|
|Time series estimating function||ts_period_detect||Estimates time series data by period.|
|Change point detection function||ts_cp_detect||Finds intervals with different statistical characteristics from time series data. The interval endpoints are change points.|
|ts_breakout_detect||Finds the time point when statistics steeply increase or decrease from time series data.|
|Maximum value detection function||ts_find_peaks||Finds the locally maximum value of time series data in a specified window.|
|Prediction and anomaly detection functions||ts_predicate_simple||Uses default parameters to model time series data and performs simple time series prediction and anomaly detection.|
|ts_predicate_ar||Uses an autoregressive model to model time series data and performs simple time series prediction and anomaly detection.|
|ts_predicate_arma||Uses an autoregressive moving model to model time series data and performs simple time series prediction and anomaly detection.|
|ts_predicate_arima||Uses an autoregressive moving model with differences to model time series data and performs simple time series prediction and anomaly detection.|
|ts_regression_predict||Accurately predicts the long-run trend for a single periodic time series.|
|Time series 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 pieces of time series data.|
|ts_hierarchical_cluster||Uses a hierarchical clustering method to cluster multiple pieces of time series data.|
|ts_similar_instance||Queries curves that are similar to a specified curve.|
|Pattern mining||Frequent pattern statistics||pattern_stat||Mines representative combinations of attributes among the given multi-attribute field samples to obtain the frequent pattern in statistical patterns.|
|Different pattern statistics||pattern_diff||Finds the patterns of differences between two collections under specified conditions.|
|Root cause analysis function||rca_kpi_search||When a time series metric is abnormal, you can use this function to quickly analyze the dimension attributes that result in the abnormal metric.|
|Correlation analysis functions||ts_association_analysis||Quickly finds the metrics that are correlated with a specified metric among multiple observed metrics in the system.|
|ts_similar||Quickly finds metrics correlated with specified time series data from multiple observed metrics in the system.|