What is ARIMA?

ARIMA, standing for Autoregressive Integrated Moving Average, is a widely used statistical method for time series forecasting. It combines three key components to model data:

1. Autoregression (AR): This component relates the present value to its past values through a regression equation.
2. Differencing (I for Integrated): It involves differencing the time series data to make it stationary, ensuring that the mean and variance are constant over time.
3. Moving Average (MA): This component uses the dependency between an observation and a residual error from a moving average model applied to lagged observations.

Time series data analysis plays a pivotal role in various fields such as finance, economics, weather forecasting, and more. The Autoregressive Integrated Moving Average (ARIMA) model stands as one of the fundamental tools for forecasting future values based on historical patterns within time series data. However, selecting the appropriate parameters for an ARIMA model is crucial to ensure accurate predictions.