Predictive Statistics
Introduction
Predictive statistics is a branch of statistics that deals with extracting information from data and using it to predict future trends and behavior patterns. The core of predictive statistics lies in the ability to quantify the uncertainty associated with these predictions.
Predictive Modeling
Predictive modeling uses statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. In many cases the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data.
Types of Predictive Models
There are several types of predictive models which include, but are not limited to, regression models, time series models, machine learning models, and neural networks. Each of these models has its own strengths and weaknesses, and is used in various fields of study.
Predictive Analytics
Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
Applications of Predictive Analytics
Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, pharmaceuticals and other fields.
Statistical Techniques in Predictive Statistics
There are several statistical techniques used in predictive statistics which include, but are not limited to, correlation, regression, classification, clustering, and dimensionality reduction.
Challenges in Predictive Statistics
Predictive statistics is not without its challenges. These include, but are not limited to, the availability and relevance of data, the quality of the predictive model, and the interpretation of the results.