In this latest update, I’ll pick up right where I left off in the previous one. I’ll share how I integrated the Augmented Dickey-Fuller (ADF) and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests into my project using Python. Additionally, I’ll discuss the interpretations and insights I gained from the outcomes of these tests.
Here is the snippet of the Python Code:
Based on the output we can interpret the following:
Starting with the ADF test results, we find that the ‘logan_passengers’ and ‘total_jobs’ series have p-values of 0.985 and 0.948 respectively, which are well above the common significance level of 0.05. This means we cannot reject the null hypothesis of a unit root, suggesting that these series are non-stationary. The implications are significant — it indicates that the data may contain trends or unit roots that could affect the accuracy of our forecasts.
On the other hand, the ‘hotel_avg_daily_rate’ has an ADF statistic of -3.597 with a p-value of 0.0058, which is below the 0.05 threshold. This suggests that the time series is stationary, and we can reject the null hypothesis of a unit root presence.
Turning to the KPSS test, the scenario is somewhat reversed. For ‘logan_passengers’ and ‘total_jobs’, the KPSS statistic is above the critical value, and the p-value is at 0.01, indicating we can reject the null hypothesis of stationarity at the 1% level. This reinforces the ADF test results, confirming that these series are indeed non-stationary. However, ‘hotel_occup_rate’ and ‘hotel_avg_daily_rate’ showed KPSS p-values higher than 0.05, suggesting we cannot reject the null hypothesis, which is consistent with the series being stationary.
This dual approach of using both ADF and KPSS tests gives us a robust understanding of our time series data’s stationarity. For ‘hotel_avg_daily_rate’, which appears to be stationary, we can proceed with further analysis or forecasting without the need for differencing or detrending. However, for ‘logan_passengers’ and ‘total_jobs’, we might consider differencing the data to remove the non-stationarity or apply models that account for trends and seasonal components.
In our next update, we will explore about forecasting and its uses in our project.