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What is ARIMA Modeling?

ARIMA is an acronym for “autoregressive integrated moving average.” It’s a model used in statistics and econometrics to measure events that happen over a period of time. The model is used to understand past data or predict future data in a series.
What is ARIMA Modeling?

To understand ARIMA, it’s helpful to examine the name. The “AR” stands for autoregression, which refers to the model that shows a changing variable that regresses on its own prior or lagged values. In other words, it predicts future values based on past values.

The “I” stands for integrated, which means it observes the difference between static data values and previous values. The goal is to achieve stationary data that is not subject to seasonality. That means the statistical properties of the data series, such as mean, variance and autocorrelation, are constant over time. Data scientists use an Augmented Dickey-Fuller (ADF) test to determine whether the data is stationary.

Finally, “MA” represents the moving average, which is the dependency between an observed value and a residual error from a moving average model applied to previous observations.

The ARIMA model is becoming a popular tool for data scientists to employ for forecasting future demand, such as sales forecasts, manufacturing plans or stock prices. In forecasting stock prices, for example, the model reflects the differences between the values in a series rather than measuring the actual values.

ARIMA Model - Complete Guide to Time Series Forecasting in Python | ML+
Using ARIMA model, you can forecast a time series using the series past values. In this post, we build an optimal ARIMA model from scratch and extend it to Seasonal ARIMA (SARIMA) and SARIMAX models. You will also see how to build autoarima models in python

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