Overview
Autocorrelation (also known as serial correlation) measures the relationship between a variable's current value and its past values. It is a fundamental concept in time series analysis.
Interpretation
- Positive Autocorrelation: High values tend to be followed by high values (and low by low).
- Negative Autocorrelation: High values tend to be followed by low values (and vice versa).
Use in Modeling
Autocorrelation plots (ACF) are used to identify patterns, seasonality, and to determine the parameters for models like ARIMA.