A form of regression analysis in which the dependent variable is related to past values of itself at varying time lags. An autoregressive model would express the forecast as a function of previous values of that time series data (e.g., Y_{t} = *a* + *b*Y_{t-I} + *e*_{t}, where *a* and *b *are parameters and *e*_{t} is an error term).