In this blog we are going to talk about AutoCorrelation Function (ACF) and implement it over Economic Indicators for the analysis.

The Autocorrelation Function (ACF) is a statistical tool that is used to determine the correlation between a time series and its own lagged values. It makes it easier to find patterns, trends, and seasonality in the data. The ACF plot displays correlation coefficients for different lags, allowing one to pinpoint significant lags and potential autocorrelation patterns in the time series. The ACF at lag k is the correlation between the time series and itself at lag k.

There are two types of ACF:

Positive and Negative ACF:

• Positive ACF suggests a positive correlation, indicating that high values at one time point may relate to high values at another time point.
• Negative ACF implies a negative correlation, indicating an inverse relationship between values at different times.

On analysis the Economic Indicators:

This analysis sheds light on the temporal relationships embedded in the ‘logan_intl_flights’ time series. The ACF plot reveals a robust positive correlation at a lag of one month, suggesting a tendency for the current month’s international flight count to positively associate with the count in the preceding month. This finding holds significance for further exploration and modeling, especially considering the application of techniques like autoregressive models designed to capture such temporal dependencies.

Notable peaks in the plot indicate substantial autocorrelation at specific lags, emphasizing the strong correlation of the time series with its past values at these particular points. The y-axis reflects both the direction and strength of autocorrelation, with a value of 1 denoting perfect positive correlation, -1 representing perfect negative correlation, and 0 indicating no correlation.