Time Series

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

Auto-correlation measures the similarity between observations of a time series over different time intervals.

Autocovariance measures how a variable correlates with itself over time, indicating its internal structure and dependencies.

A lookback window is a specified period used to analyze past data for predictions in AI and machine learning models.

A moving average smooths data by averaging values over a specified number of periods.

A statistical technique used to smooth data by averaging values over a specified number of periods.