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Glossary

Glossary

It consolidates uncommon or specialized terms used in TIM anomaly detection, is meant to save time and promote consistency.

term definition
Anomalous behavior model It is a important part of TIM anomaly detection engine, that is responsible for creating model for each detection perspective resulting in anomaly indicator.
Anomaly Data point that does not conform to the expected behavior of a given group, i.e. data point that does not fit well with the rest of the data (often referred to as anomaly, outlier, exception or contaminant).
Anomaly indicator Final output of TIM Anomaly Detection
Autocorrelation Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay.
Causation Causation indicates a relationship between two variables where one variable is affected by the other. For example, when the value of one variable, increases or decreases as a result of other variables, it is said there is causation.
Collective anomaly Type of anomaly
Configuration All available settings of TIM Anomaly Detection
Contextual anomaly Type of anomaly
Correlation Correlation is a statistical measure that reveals the degree to which two variables are linearly related (no declaration of cause and effect).
Dependency-oriented data Time-series are dependency-oriented data. In such data, anomalies are usually defined in a contextual or collective sense and are harder to distinguish from noise.
Design of experiment Design of experiment is characterized by data(chosen KPI and influencers affecting it) and domain specifics
Detection Evaluation of an existing model
Domain specifics Settings that are related to your domain
Feature Feature is a transformation(by engine) of the original variable/variables
Global anomaly Type of anomaly
Influencer Influencers(explanatory variables) are the ones which through we are trying to explain the value or effect on the KPI variable by creating a relationship between an explanatory and dependent variable.
Key performance indicator(KPI) KPI(dependent variable) is nothing but the variable of a dataset about which we want to have a deeper understanding - we are detection anomalies on this variable
Model building Creation of a new model
Model rebuilding Reconstruction of an existing model
Multivariate AD with defined KPI More dimensional AD problem, that has to be characterized by one of the columns in the data - KPI
Normal behavior model It is an important part of TIM anomaly detection engine, that is responsible for creating model characterizing the expected behavior of a given KPI
Normal behavior The output of normal behavior model representing the expected value of a given KPI
Detection perspective The aspect of how you look on anomalies.
Rebuild type Define what part of a model has to be reconstructed
Residuals A difference between actual and normal behavior values of a given KPI
Root cause analysis An interpretation of what drives normal behavior
Semi-supervised AD Type of an algorithm related to label availability
Sensitivity A number in percentage that defines the sensitivity of the underlying model to anomalies
Supervised AD Type of an algorithm related to label availability
Temporal continuity Temporal continuity refers to the fact that the patterns in the data are not expected to change abruptly, unless there are abnormal processes at work
Time-series data Time series data is a collection of observations for a single subject (entity) obtained through repeated measurements at different time intervals (generally equally spaced as in the case of metrics, or unequally spaced as in the case of events).
Univariate AD One dimensional AD problem
Unsupervised AD Type of an algorithm related to label availability
Variable By variable we mean either KPI(dependent variable) or Influencer(explanatory variable).