How to identify anomalies?

In this section, the best explainable visualisations to identify anomalies are displayed. Anomalies are deviations or irregularities in data patterns that stand out from the expected or normal behavior. This section will help you to early detect irregularities by minimising their impact before they escalate. Anomalies can manifest in diverse ways, including outliers, spikes, unexpected trends, and instances of missing data.

Bar/Line Plot

Description

Example from XMANAI

A bar/line chart combines features of bar and line charts, using bars for discrete categories and lines to indicate trends over a continuous scale.
It is useful for illustrating relationships between two datasets, allowing emphasis on individual values and the overall pattern in a single representation.

A bar chart has been used in XMANAI to compare the trend of a sensor real values and predicted values in a specific range of time to let the user compare the values and create trust in the XAI models used