Autoregression (machine learning)
Autoregression is a statistical technique used in Machine learning to model and forecast sequential data, such as Time series data, by utilizing the correlations between an observation and its past values. This approach is particularly useful in predicting future values in a sequence, based on previously observed values. Autoregression is closely related to Lasso regression and Neural (artificial) architectures, which can also be used for sequential data modeling. The technique relies on the assumption that the current value of a sequence is a function of past values, allowing for the identification of patterns and trends in the data. Autoregression has numerous applications, including Disease surveillance, Financial forecasting, and Weather forecasting, where understanding sequential relationships is crucial.