Glossary Library

Technical GlossaryMachine Learning

ARIMA

A classical model for time series forecasting that combines autoregressive and moving-average components.

ARIMA is one of the best-known classical methods in time series analysis. It produces forecasts by modeling the structure among past observations and past error terms. Stationarity assumptions and parameter selection play a critical role in its performance. It can serve as a strong starting point in scenarios involving economic indicators, sales series, and operational forecasting.

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Lag Feature

A type of feature that brings time-dependent patterns into the model using values from previous time steps.

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Rolling Window Features

A feature structure that summarizes past observations within a defined window to generate time-dependent signals.

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Linear Regression

A fundamental regression algorithm that models the linear relationship between input variables and a target variable.