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This glossary consolidates uncommon or specialized terms used in TIM Detect's documentation, and is meant to save time and promote consistency.

Anomalous behavior modelAn important part of TIM Detect's engine for kpi-driven anomaly detection, responsible for creating a model for each detection perspective resulting in corresponding anomaly indicators.
AnomalyAn observation that does not conform to the expected behavior of a given group, i.e. an observation that does not fit well with the rest of the data (often referred to as anomaly, outlier, exception or contaminant).
Anomaly indicatorAn anomaly indicator is a number in the interval (0, infinity) specifying the extent to which a given observation is anomalous. The number 1 is the anomaly indicator threshold - if the indicator is below or equal to 1, the corresponding observation is considered normal; if it is above, it is considered an anomaly. The higher the number, the more anomalous that particular observation is. The anomaly indicator(s) is(/are) a final output of TIM Detect's model building for both kpi-driven and system-driven anomaly detection.
Anomaly indicator windowA time range over which the anomaly indicator is smoothed. This smoothing happens by averaging the last n successive values, where n is determined by the length of the window (the time range).
ApproachThe form of solving the multidimensional anomaly detection problem. Currently, TIM supports a kpi-driven approach and a system-driven approach.
AutocorrelationAutocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay.
CausationCausation indicates a relationship between two variables where one variable affects another. Thus, when the value of one variable changes as a result of a change in the value of another variable, there is a causal link between these variables.
Collective anomalyA type of anomaly composed of a group of observations that are individually not anomalous (neither in a contextual, nor in a global sense), but their occurence together (as a group) is abnormal.
ConfigurationAll the available parameters to configure TIM Detect's models throughout their lifecycle. The configuration options differ between TIM Detect's kpi-driven and system-driven approaches.
Contextual anomalyA type of anomaly that occurs when one or more observations are anomalous regarding the context, meaning the values preceding it and/or the values of the influencers at the same point in time.
CorrelationA statistical measure that reveals the degree to which two variables are linearly related (without declaration of cause and effect).
Dependency-oriented dataData that contains observations that may be linked to eachother by implicit or explicit relationships. Time series are dependency-oriented data that often contain implicit dependencies: two successive observations are likely related to eachother, therefore, the time attribute implicitly specifies a dependency between them. In such data, anomalies are usually defined in a contextual or collective sense and are harder to distinguish from noise.
Experiment designThe choices made given the context of an experiment, characterized by the data (chosen KPI (if any), influencers) and the domain specifics.
DetectionThe evaluation or application of an existing model, typically on new data, to detect anomalies in that data.
Detection perspectiveA viewpoint from which to look at anomalies; each detection perspective is dedicated to identify a different type of anomalies. Detection perspectives come into play in TIM Detect's kpi-driven anomaly detection.
Domain specificsSettings that are related to the domain of the use case and the data. These settings differ between TIM Detect's kpi-driven and system-driven approaches.
FeatureA transformation (by the TIM engine) of the original variable/variables.
Global anomalyA type of anomaly that occurs when an observation deviates strongly from most observations of a given dataset. It is also called a global outlier.
InfluencerInfluencers, also known as explanatory or independent variables, are variables influencing the KPI, also known as the dependent variable.
In-sample periodThe period used for configuring, training and creating a model.
Key performance indicator (KPI)The KPI, also know as the dependent variable, is the variable on which the detection focuses; anomalies are detected on this variable (with the kpi-driven approach).
ModelA representation of reality (data).
Model buildingThe process that creates of a new model.
Model rebuildingThe reconstruction of an existing model
Multivariate AD with kpi-driven approachAn anomaly detection problem on a dataset with multiple variables, focusing on anomalies in one of the variables (the KPI).
Multivariate AD with system-driven approachAn anomaly detection problem on a dataset with multiple variables, focusing on anomalies over the entire group of variables.
Normal behaviorThe output of a normal behavior model representing the expected value of a given KPI
Normal behavior modelAn important part of TIM Detect's engine for kpi-driven anomaly detection, responsible for creating model characterizing the expected (normal) behavior of a given KPI.
Out-of-sample periodThe period used for evaluating a model's performance.
Rebuild typeThe type of rebuild that is peformed for a rebuild model job, defining what part of the model has to be reconstructed.
Root cause analysisAn interpretation of what drives normal behavior.
Semi-supervised ADA type of algorithm for anomaly detection problems where a model representing normal behavior is contructed from a given normal training data set, and then used to test the likelihood that a test instance is generated by the learned model.
SensitivityA percentual number that defines the ratio of observations in the in-sample period that is expected to be anomalous; in such, it represents the sensitivity of the underlying model to anomalies. The sensitivity relates to each of the detection perspectives in the kpi-driven approach as well as to the system-driven approach.
ResidualsThe difference between actual values and normal behavior values of a given KPI.
Supervised ADA type of algorithm for anomaly detection problems on data for which all training observations are labeled as either "normal" or "anomalous" prior to training. This involves training a classifier, with the key difference to many other classification problems lying in the inherently unbalanced nature of outlier detection.
SystemThe process of a given problem.
Temporal continuityRefers to the fact that the patterns in the data are not expected to change abruptly, unless there are abnormal processes at work.
Time-series dataTime-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 ADA one dimensional anomaly detection problem, meaning an anomaly detection problem on a single variable.
Unsupervised ADA type of algorithm for anomaly detection problems where there is no label available. (It is not known in advance which of the training observations are anomalous and which are normal.)
VariableA column in the dataset with the potential of characterizing the system, this can be either a KPI (dependent variable) or an influencer (explanatory variable).