Resolved: How many lags should be determined?
Hi, Abdulrahman Nasser Albadi,
Thanks for reaching out. When determining the number of lags to include in your time series model, the Autocorrelation Function (ACF) plot can indeed provide valuable insight. The key signs to look for when analyzing the ACF are:
Significant Lags: Look for lags where the ACF values significantly exceed the confidence interval (usually represented by dashed lines on the plot). These indicate lags that might be useful to include in your model.
Cut-off Point: If the ACF values drop to near zero and remain within the confidence interval after a certain lag, this can indicate the point at which to stop including additional lags.
Shape of the ACF: A rapidly decreasing ACF might suggest an AR(p) model, while a slowly decaying ACF might imply an ARIMA model with a non-zero integrated component.
Thresholds and Patterns: Identify any clear patterns (e.g., a sinusoidal pattern) that might suggest seasonal effects which need to be accounted for with additional lags.
Cross-reference with PACF: Additionally, it can be beneficial to compare the ACF with the Partial Autocorrelation Function (PACF), as it helps confirm the number of lags for autoregressive components.
By carefully analyzing these signs, you can arrive at a well-informed decision about the inclusion of lags in your model. Feel free to ask if you have any more questions!
Best of luck with your analysis!