Autoregressive moving average

An autoregressive integrated moving average, or ARIMA, is a statistical analysis model that uses time series data to either better understand the data set or to predict future trends. A statistical model is autoregressive if it predicts future values based on past values.

1. What is ARMA model used for?
2. What is the difference between moving average and autoregressive?
3. What is the difference between AR and ARMA?
4. What is ARMA in forecasting?

What is ARMA model used for?

AR, MA, ARMA, and ARIMA models are used to forecast the observation at (t+1) based on the historical data of previous time spots recorded for the same observation. However, it is necessary to make sure that the time series is stationary over the historical data of observation overtime period.

What is the difference between moving average and autoregressive?

A Moving Average model is similar to an Autoregressive model, except that instead of being a linear combination of past time series values, it is a linear combination of the past white noise terms.

What is the difference between AR and ARMA?

ARMA is the combination of the AR and MA models. ARMA models cover both aspects of AR and MA. The ARMA model predicts the future values based on both the previous values and errors. Thus ARMA has better performance than AR and MA models alone.

What is ARMA in forecasting?

ARMA is a model of forecasting in which the methods of autoregression (AR) analysis and moving average (MA) are both applied to time-series data that is well behaved. In ARMA it is assumed that the time series is stationary and when it fluctuates, it does so uniformly around a particular time.