Autoregression (statistics)
Autoregression is a statistical technique used to model and forecast time series data, where the current value of a variable is predicted based on past values of the same variable. This method is commonly used in Time Series Analysis to identify patterns and trends in data. Autoregression is closely related to Polynomial Regression and Linear Regression, but it focuses on the relationships between consecutive values in a time series. The autoregressive model is often used in Forecasting and Evaluation of economic and financial data, such as stock prices and weather patterns. Ashish Vaswani and other researchers have applied autoregressive techniques in Natural Language Processing and Transformer Architecture.