what is an autoregressive integrated moving average arima 
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 for example an arima model might seek to predict a stock s future prices based on its past performance or forecast a company s earnings based on past periods understanding autoregressive integrated moving average arima an autoregressive integrated moving average model is a form of regression analysis that gauges the strength of one dependent variable relative to other changing variables the model s goal is to predict future securities or financial market moves by examining the differences between values in the series instead of through actual values an arima model can be understood by outlining each of its components as follows 1arima parameterseach component in arima functions as a parameter with a standard notation for arima models a standard notation would be arima with p d and q where integer values substitute for the parameters to indicate the type of arima model used the parameters can be defined as 1for example a linear regression model includes the number and type of terms a value of zero 0 which can be used as a parameter would mean that particular component should not be used in the model this way the arima model can be constructed to perform the function of an arma model or even simple ar i or ma models 23because arima models are complicated and work best on very large data sets computer algorithms and machine learning techniques are used to compute them arima and stationary datain an autoregressive integrated moving average model the data are differenced in order to make it stationary a model that shows stationarity is one that shows there is constancy to the data over time most economic and market data show trends so the purpose of differencing is to remove any trends or seasonal structures 1seasonality or when data show regular and predictable patterns that repeat over a calendar year could negatively affect the regression model if a trend appears and stationarity is not evident many of the computations throughout the process cannot be made and produce the intended results a one time shock will affect subsequent values of an arima model infinitely into the future therefore the legacy of the financial crisis lives on in today s autoregressive models 