I have been working with datasets for Economic Indicators which I retrieved from Analyze Boston. In my previous blog, I talked about various method of Time Series Forecasting. I have applied those methods to check the Stationarity and Differencing of Logan-International flights time-series.

Stationarity:

Stationarity is an important concept in time series analysis. A stationary time series is one that has constant statistical characteristics over a given period of time. The lack of seasonality or trends simplifies the modelling process.
To achieve stationarity and stabilise statistical properties, transformations such as differencing or logarithmic transformations are frequently required.

By visualizing the above graph, it doesn’t look stationary. But we can check the stationarity by using ADF test. The Augmented Dickey-Fuller (ADF) test is a well-known solution to this issue. This statistical tool determines whether a unit root is present, indicating non-stationarity, through a thorough examination. If a unit root’s null hypothesis is less than the conventional 0.05 threshold, stationarity is confirmed. Our understanding of the dataset’s temporal dynamics is enhanced by the comprehensive approach’s integration of statistical rigor and domain expertise.

Differencing:

It is an method to achieve stationarity in time-series. It involves calculating the variations between successive observations. Through the removal of seasonality and trends, this process enhances the time series’ analytical accessibility. The difference in the first order is Y (t) – Y (t1).

This is how first order differenced series looks wrt original time series. By visualizing it looks differencing has been achieved and the time series looks stationary.