Howtosignalprocessing
- What are ARMA models used for?
- Why might ARMA models be considered particularly useful for financial time series?
- Which ARMA model is the best?
- What does ARMA actually mean for a sample time series?
What are ARMA models used for?
The autoregression and moving average (ARMA) models are used in time series analysis to describe stationary time series . These models represent time series that are generated by passing white noise through a recursive and through a nonrecursive linear filter , consecutively .
Why might ARMA models be considered particularly useful for financial time series?
ARMA models are of particular use for financial series due to their flexibility. They are fairly simple to estimate, can often produce reasonable forecasts, and most importantly, they require no knowledge of any structural variables that might be required for more “traditional” econometric analysis.
Which ARMA model is the best?
To select the best ARIMA model the data split into two periods, viz. estimation period and validation period. The model for which the values of criteria are smallest is considered as the best model. Hence, ARIMA (2, 1, and 2) is found as the best model for forecasting the SPL data series.
What does ARMA actually mean for a sample time series?
In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression (AR) and the second for the moving average (MA).
