Time-Series AI/ML

Time Series AI Machine Learning

Apica Ascent's baselining feature studies the query data in time series format using machine learning algorithms to implement Anomaly detection and forecasting of the data based on historical data. This feature also includes the mathematical implementation of moving averages and moving standard deviation on the query data. The above feature makes it easy for the user to visualize the behavior of the data from a time-series perspective.

This feature can be mainly divided into four categories namely

• Anomaly detection

• Forecasting

• Moving Average

• Moving Standard Deviation.

Basic Baselining Configuration Guide:

The baselining configuration dialog box appears on the builder Panel under Algorithms section. This configuration mainly needs a time column to indicate the time series data column in the dataset, and a value column to indicate the target value column dependent on the time series. The feature studies the dependent value column from the perspective of the time column and produces the required results which can be visualized using the Plot section to create a meaningful charts.

You can visualize the results by navigating to the Plot section and adding the necessary columns to the Y-axis selector to generate meaningful charts. These visualizations help in analyzing trends and identifying patterns in your time series data.