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Auto Regressive Integrated 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. 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.
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The name “ARIMA” reflects the three components of the model: autoregressive (AR), 
integrated (I), and moving average (MA). 
Autoregressive (AR): The autoregressive component refers to the relationship between the 
current value of a variable and its past values. It assumes that the future values of the variable can 
be predicted based on a linear combination of its own past values. 
Integrated (I): The integrated component deals with the differencing of the time series data 
to make it stationary. Stationarity means that the statistical properties of the time series, such as 
mean and variance, do not change over time. Differencing involves taking the difference between 
consecutive observations to remove trends or seasonality. 
Moving Average (MA): The moving average component considers the dependency between 
the error term and past errors. It assumes that the future values of the variable can be predicted 
based on a linear combination of past error terms. 
ARIMA models have been widely used in various fields, including finance, economics, and 
environmental sciences, to analyze and predict time series data. However, it's important to note 
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https://www.investopedia.com/terms/a/autoregressive-integrated-moving-average
arima.asp#:~:text=An%20autoregressive%20integrated%20moving%20average%2C%20or%20ARIMA%2C%20
is%20a%20statistical,values%20based%20on%20past%20values. 

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