The ARIMA (AutoRegressive Integrated Moving Average) model is a powerful method for forecasting time series data, encompassing three principal elements. The AutoRegressive (AR) part captures the links between an observation and a specified number of its previous values, denoted by ‘p’. A larger value of ‘p’ means the model accounts for more complex, long-term dependencies. The Integrated (I) aspect involves differencing the data to ensure stationarity, a critical step in time series analysis. The differencing order is indicated by ‘d’, showing the number of times differencing is performed. The Moving Average (MA) portion looks at the correlation between an observation and the residual errors from a previous moving average model, with ‘q’ denoting the number of lagged residuals involved. This model is typically described as ARIMA(p, d, q).
ARIMA models are extensively used across various fields, including finance and environmental studies, for analyzing time-dependent datasets. The process of using an ARIMA model involves initial data exploration, checking for stationarity, selecting appropriate parameters, training the model, and then proceeding to validation, testing, and forecasting. These models are essential tools for analysts and data scientists, providing a structured approach to conducting robust time series forecasting and analysis.