ARIMA Models
ARIMA (AutoRegressive Integrated Moving Average) models are a class of statistical models used for analyzing and forecasting time series data. They combine autoregressive (AR) terms to model the relationship between an observation and its lagged values, differencing (I) to make the time series stationary, and moving average (MA) terms to model the relationship between an observation and residual errors from previous observations. These models are widely applied in fields like economics, finance, and supply chain management to predict future values based on historical patterns.
Developers should learn ARIMA models when working on projects involving time series forecasting, such as predicting stock prices, sales trends, or weather patterns, as they provide a robust framework for handling non-stationary data with trends and seasonality. They are particularly useful in data science and machine learning applications where historical data is available and future predictions are needed, offering interpretability and flexibility through parameters like p, d, and q. However, they may require preprocessing steps like differencing and assumption checks for optimal performance.