ARIMA differs from other longitudinal analysis techniques in the social sciences such as latent growth modelling ( LGM ) and multi - level modelling ( MLM ) in a number of ways . First , it focuses on a singleunit dependent variable over time . It is suitable in situations where there is suitable aggregate data for a phenomenon , but where individual data may be sparse or incomplete ( as is the case in this study ) . Second , while LGM and MLM tend to be applied on data sets with only a few time periods , ARIMA is most suitable for time series with more than 50 time points ( Yanovitzky and VanLear , 2007 ) . Third , neither LGM or MLM adequately reflect the time series character of a data set and do not capture trends that are caused by the internal dynamics of the variable under study ( Hollanders and Vliegenthart , 2008 ) . This limitation is typically addressed via research designs with a small number of distinct time data points ( often two , three or four ) or with panel data sets ( Shin , 2017 ) . Notwithstanding , a key problem in such designs is that variables are expected to vary at random , so the common practice of selecting data every three or four time units ( Wilson et al . , 2006 ) , can easily result in the incorrect identification of a trend ( Kelly and McGrath , 1988 ) . ARIMA is designed to account for the complex internal dynamics of time series variables through the different mechanisms it employs , as explained above .