What is the relation between ARIMA and time series analysis?

ARIMA, which stands for AutoRegressive Integrated Moving Average, is a powerful and widely used statistical model specifically designed for analyzing and forecasting time series data. Time series analysis involves examining data points collected or recorded at specific time intervals to identify patterns, trends, and seasonal variations over time.

The strength of ARIMA lies in its ability to model the underlying structure of time series data by capturing the temporal dependencies, ensuring that predictions account for the historical values in the series. Through its components — autoregressive (AR), differencing (I), and moving average (MA) — ARIMA effectively accounts for trends and seasonality, making it particularly suited for forecasting future values based on the knowledge of past data.

Other options do not accurately represent the purpose and application of ARIMA. While unsupervised learning focuses on finding hidden patterns in data without specific outcomes, ARIMA is focused on forecasting based on prior time points. It is not a linear regression technique, as its framework is built specifically for time series data rather than a general linear relationship, and ARIMA primarily works with univariate time series (one variable over time) rather than requiring multivariate data, which involves multiple variables influencing one another. Thus, the primary purpose of ARIMA aligns with time series