Autoregressive Models

Autoregressive models are a class of statistical models used for analyzing and predicting time series data. They operate on the principle that the current value of a variable can be explained as a function of its previous values. Key characteristics include the use of lagged observations as inputs and the assumption that past data can help forecast future values. Common applications include economic forecasting, stock price prediction, and natural language processing tasks, where the model predicts the next word in a sequence based on previous words. These models can be simple linear regressions or more complex neural network architectures.