An autoregressive (AR) model is a statistical model in which the variable of interest regresses on itself in a linear regression model. In other words, an AR model uses a time series to predict its own future.
In artificial intelligence and machine learning, autoregressive models are commonly used in time series analysis to predict the future value of a variable based on its past values. In this type of model, the variable of interest is decomposed into a combination of its past values and a random error term.
The order of an AR model refers to the number of past values used to predict the future value of the variable of interest. For example, an AR model of order 1 uses only one past value to predict the future value, while an AR model of order 2 uses two past values.
Autoregressive models are useful for predicting patterns and trends in time series, such as stock prices, energy consumption or web traffic. The accuracy of an AR model depends largely on the choice of the order and amount of data available for training the model.
Deep learning translates as deep learning and is a type of artificial intelligence (AI) that is encompassed within machine learning.
Read More »How is artificial intelligence helping us? Artificial intelligence (AI) has gone from being the stuff of science fiction movies to a [...]
Read More »In the digital age in which we live, artificial intelligence (AI) has emerged as a disruptive force in numerous industries, and the banking sector has been [...]
Read More »We often wonder where Big Data is applied and we can assume a great relevance of Big Data for business. This explains the great in [....]
Read More »