AUTOREGRESSION Meaning and
Definition
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Autoregression refers to a statistical modeling technique in time series analysis where the current value of a variable is predicted solely based on its past values. It is an extension of the concept of linear regression, but adapted to analyze patterns and trends in a time-dependent dataset. Autoregression assumes that the relationship between the variable and its past values can be modeled using a linear equation.
In autoregression, the variable of interest, known as the dependent variable, is regressed against one or more lagged values of itself, known as independent variables. The order of autoregression, denoted by "p," represents the number of lagged values included in the model. This determines the level of dependency on past values and helps capture important patterns in the time series data.
The autoregressive coefficient(s) estimated in the model represent the impact of each lagged value on the current value of the variable. By estimating these coefficients through techniques like ordinary least squares, the autoregression models can be used to predict future values of the variable.
Autoregression is commonly used in various fields, especially in economics and finance, to analyze and forecast time-dependent data. It provides insights into the persistence, memory, and patterns in a time series. Autoregressive models aid in identifying trends, cycles, and potential future outcomes, making them valuable tools for forecasting and decision-making in diverse industries.
