ARIMA, which stands for AutoRegressive Integrated Moving Average, is a statistical method used in time series analysis to forecast future values based on historical data. This method is widely used in business analytics, particularly in the fields of finance, economics, and marketing, to make informed decisions based on trends and patterns in the data.
The ARIMA model is built on the assumption that the future values of a time series can be predicted based on its past values and the errors that have been made in previous predictions. The model takes into account the trends and patterns in the data, as well as any seasonality or cyclical patterns that may exist.
One of the key advantages of the ARIMA model is its ability to handle non-stationary data, which is data that changes over time and does not have a constant mean and variance. The ARIMA model uses a technique called differencing, which removes non-stationary trends from the data, allowing for more accurate predictions.
In order to build an ARIMA model, you must first identify the order of differencing needed to make the data stationary. You also need to identify the number of autoregressive (AR) terms and moving average (MA) terms to include in the model, which are determined through a process called model selection.
Once the ARIMA model has been built, you can use it to make predictions about future values in the time series. The model uses the historical data and the identified trends and patterns to make predictions about future values, which can then be used to make informed decisions.
In a nutshell, I would like to say that the ARIMA model is a powerful tool for business analytics that allows you to make accurate predictions about future values in time series data. Whether you’re analyzing sales data, financial data, or any other type of data that changes over time, the ARIMA model can provide valuable insights and help you make informed decisions.
Population Forecast of Bangladesh: An experimentation using ARIMA modelling approach
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