Auto-regressive Models, also known as AR Models, are a class of predictive techniques used in time series analysis. The term auto-regressive originates from the mathematical model, and it refers to the property of a predictor being able to predict its own output. This specialized technique is based on the assumption that the current output of a system can be predicted based on its past behavior.
Auto-regressive models often operate by employing a mathematical model that assumes each output in a system over the course of a temporal period is related to the outputs in preceding time periods. An AR Model might require an input of the past 200 days of a stock market or monthly sales figures during a sales period. The model then creates a numerical prediction or forecast from the data, which can be used to develop strategies for future prediction.
The application of auto-regressive models is wide-ranging, such as in speech recognition, economic forecasting, and weather forecasting. Due to its vast reach, this model has become popular in the world of computer science. Specifically, AR models have been used to balance supply and demand in multi-echelon inventory systems.
In summary, auto-regressive models are a data science technique wherein the current output of a system is predicted based upon its past behavior. This is achieved by employing a mathematical model that assumes each output in a system over a temporal period is related to the outputs in preceding time periods. Auto-regressive models have been used in a variety of industries, ranging from manufacturing to economics and weather forecasting, and have become increasingly popular due to their vast reach and application in the field of computer science.
