Forecasting

ARIMA Model is a statistical method used for time series forecasting, combining autoregression, integration, and moving averages.

Autoregressive Integrated Moving Average (ARIMA) is a statistical analysis model used for forecasting time series data.

An autoregressive model predicts future values based on past values in a time series.

Exponential Smoothing is a forecasting technique that uses weighted averages of past data to predict future values.

Forecasting Error refers to the difference between predicted and actual values in predictive models.

Mean Absolute Percentage Error measures the accuracy of a forecasting model as a percentage.

Multi-step forecasting predicts future values over multiple time steps based on historical data, often using advanced AI techniques.

Naive Forecast is a simple forecasting method that uses past data to predict future values without complex models.