Autocorrelation Function (ACF) plots for various time series data. These plots show the correlation of the time series with its own past values at different lags (time intervals). The blue shaded area in the plot represents the confidence interval, typically set at 95%. Correlation values outside of this area are considered statistically significant.
Here’s a brief explanation for each graph:
Logan Passengers: The ACF plot for ‘Logan Passengers’ shows that there are a few lags where the correlation is significant. This suggests that past values of the series have some correlation with future values, hinting at a potential AR process.
Logan Intl Flights: The ‘Logan Intl Flights’ ACF plot indicates significant autocorrelation at a few initial lags. This might suggest an autoregressive component in the time series, which could be used in model identification.
Hotel Occupancy Rate: This plot shows several significant spikes, suggesting a strong seasonal pattern or an AR component that repeats at regular intervals.
Labor Force Participation Rate: The ACF for ‘Labor Force Participation Rate’ shows fewer significant correlations, indicating that the series might not be strongly dependent on its past values, or it might be a more complex model that does not fit neatly into an AR or MA process.
Hotel Average Daily Rate: The plot displays very few significant correlations at specific lags, which may suggest that the series has a less pronounced AR structure.
Total Jobs: Significant correlations between a few initial lags are visible, which could indicate an AR process at work. The data might be influenced by its values in the near past.
Unemployment Rate: The ACF plot shows almost no significant autocorrelation at any lag, suggesting that past values do not have a strong linear relationship with future values.