Components of ARIMA Model

Running head: STATISTICS
Statistics
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1STATISTICS
Table of Contents
Introduction......................................................................................................................................2
Discussion........................................................................................................................................2
Components of ARIMA Model...................................................................................................2
Conclusion.......................................................................................................................................3
References........................................................................................................................................4

2STATISTICS
Introduction
An Autoregressive Integrated Moving Average or ARIMA is a key statistical model that
applies the various time series data available in order to better understand analyse and review the
given data set and thereby predicting the future trends from the same. The Autoregressive
Integrated Moving Average (ARIMA) is in the form of a regression analysis that reflects, shows
or gauges the strength of a dependent variable in relation to the other variable or components that
are undertaken for the purpose of analysis (Baquero, Santana & Chiaravalloti-Neto, 2018). The
goal of the model is done for predicting the trend of future securities and movement of the
financial market with the help and analysis of taking the differences amongst the value in series
model instead of taking the actual values throughout.
Discussion
Components of ARIMA Model
The components of the ARIMA Model can be well described as follows:
 Auto Regression (AR): The term refers to the model that would be reflecting a change
in the variable thereby regressing the variable on its own lagged value or prior values.
 Integrated (I): It represents the differencing of the various raw observations that are
undertaken in order to the fact that the time series data becomes stationary, i.e., the
data values are replaced with the arising differences between the data values and there
previous values undertaken (Loch, Janczura & Weron, 2016).
 Moving Average (MA): The averages includes the interdependence amongst the
observations and residual errors from a moving average model that are generally
applied to their lagged observations.