Predict v2.1 Overview

The Logi Predict add-on module for Logi Info allows users to analyze historical or transactional data and make statistical predictions about current data. Logi Predict uses the "R" statistical analysis environment to process data and offers several different analytic methods for prediction processing.  

The Logi Predict v2.1 Help System provides you with everything you need to know to fully utilize all of the features of Predict v2.1. This topic provides a brief explanation of Predict v2.1, and you can access other relevant information using the links we have listed in this topic, or by looking at the Table of Contents, or by using Search.

For information about hardware and software requirements for Predict v2.1, see: About Logi Predict v2.1.

The Predictive Process

The two-step process used by Logi Predict is shown in the following diagram: 

In Step 1, historical or transactional data is processed using predictive algorithms to create a "prediction model", a process referred to as training a model. The model contains information about the patterns and other statistical indicators discovered during its analysis of the data. Logi Predict offers four types of models: Classification, Clustering, Forecast, and Outliers, which are discussed later on. A model only includes the results of its analysis of the historical data, not the historical data itself. Once created and trained, models are stored and can be re-used.

In Step 2, the model is applied to the "new" data, the data about which we want predictions, by creating a "prediction plan". The model applies the patterns and indicators it learned in Step 1 to the new data, resulting in predictions. This step can be scheduled to run repeatedly against new or changing data over time.