# Auto Regressive Integrated Moving Average, the Model Explained.

You might have heard the phrase “auto regressive integrated moving average” in passing when looking through investment forums or magazines. This model is also known as ARIMA. The basic principle of this model is to predict behavior into the future based on past performance. In the world of economics and investing, it is a model often used to predict how well a certain investment or security will perform into the future based on the values of the past.

The ARIMA model has many uses outside of investing or economics. It is a general model that looks at the differences between successive values instead of the values themselves.

For example, let’s say you had three values that you measured successively: 2, 4, 5. The ARIMA model would look at the differences between the first and second values as well as the second and third. The difference between 2 and 4 is 2. The difference between 4 and 5 is 1. The model would look at those differences: the 2 and the 1. The model would try to predict, based on those past differences, what the next observed value in the series would be.

When you look at an investment, you want to have a good idea of what the future holds as to its value. In order to do that you can use the auto regressive integrated moving average model to predict the future value of the investment based on past performance. In order to do that, you need to have a good set of data. Many statisticians want at least 40 data points to get a good calculation within the ARIMA model. The more data points you have, the more likely the prediction will prove accurate.

Let’s say you are looking at a particular stock price. You can get stock prices for the past 40 weeks and plug it into an ARIMA model. With that data, the model can predict what the future trends for the stock price will be in the future. Of course, the further into the future that you try to predict, the less likely the prediction will be accurate.

Most investors use the ARIMA model for those types of investments that tend to have a narrow range of fluctuation. When looking at highly volatile investments, the ARIMA model is not accurate enough to use for any type of forecasting. Most investors use the ARIMA as one tool in their investment analysis process.

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