Autoregressive integrated moving-average (ARIMA) models are models which can be fitted to a single time series and used to make predictions of future observations. They owe their popularity primarily to the work of Box and Jenkins (1970), who defined the class of ARIMA and seasonal ARIMA models and provided a methodology for selecting a suitable model from that class.
- Box, G.E.P., and G.M. Jenkins. 1970. Time series analysis: Forecasting and control. San Francisco: Holden-Day.Google Scholar
- Engle, R.F. 1978. Estimating structural models of seasonality. In Seasonal analysis of economic time series, ed. A. Zellner, 281–308. Washington, DC: Bureau of the Census.Google Scholar
- Harvey, A.C. 1985. Trends and cycles in macroeconomic time series. Journal of Business and Economic Statistics 3: 216–227.Google Scholar
- Harvey, A.C., and P.H.J. Todd. 1983. Forecasting economic time series with structural and Box–Jenkins models: A case study (with discussion). Journal of Business and Economic Statistics 1: 229–315.Google Scholar