In this blog we are going to analyze the trend and discuss about seasonality.

In the below graphical representation we have plotted the historical data of international flights at Boston Logan airport.

There is an initial upward trend between 2013 and 2015, suggesting a gradual increase in the number of international flights.
The plot steepens from 2015 to 2016, suggesting a quicker rise in international flights during this time frame. This might point to a time of explosive expansion or a change in the driving forces.
The plot stays at the same angle from 2016 to 2018 as it did from 2013 to 2015. Unlike the earlier period, when growth was more markedly increased, this pattern of sustained growth shows growth at a relatively constant rate.

We could also compute and plot a moving average or apply more sophisticated time series decomposition techniques to uncover underlying trends. These methods can help uncover any long-term trends or variations in the number of international flights, offering insightful information about the long-term dynamics of the airport.

The Orange Line in Context: The data trends over the designated window size are represented by the peaks and valleys of the orange line. An increasing trend over the chosen time period is indicated if the orange line rises. A downward trend is indicated if it is falling.
The variations are less sharp than in the original data, which facilitates the identification of broad trends.
Usually, the light orange area indicates a confidence interval around the moving average. The data points within each window are more erratic or uncertain when the confidence interval is wider.

We can do the Seasonal-trend decomposition of the international flights and can identify which specific time (month or year) has the highest flights.
For instance if we explicitly set the interval of time series to months instead of years. we can see the frequency of flights in each month.

The trend, seasonal and residual graph will be :

Using the seasonal component derived from STL decomposition instead of the original data allows us to highlight and isolate the recurrent patterns present in international flight numbers. The seasonal component allows us to focus on the recurring variations associated with different months by representing the regular, periodic fluctuations.

Through an analysis of this component, we can pinpoint particular months that exhibit a consistent increase or decrease in the number of international flights. This helps us to better understand the seasonal patterns and trends present in the dataset. With the use of this technique, recurrent behaviours that could be obscured or diluted in raw, unprocessed data can be found.

The tallest bar shows the average seasonal component for the month with the highest value. This shows that month has the highest average number of flights during the season. Conversely, shorter bars indicate periods with fewer international flights during months with lower average seasonal effects. By examining the heights of these bars, one can gain insight into seasonal variations and determine which months exhibit a consistent increase or decrease in the number of international flights, based on the seasonal patterns that were extracted from the data.