The fluctuations that are apparent in the second graph can be explained by predictable seasonal events and influences, which economists refer to as the seasonality of data.

The Bureau of Labor Statistics describes seasonal factors such as "the size of the labor force, the levels of employment and unemployment, and other measures of labor market activity undergo fluctuations due to seasonal events including changes in weather, harvests, major holidays, and school schedules" (BLS). Four major categories that encompass the factors of seasonality are calendar, timing decisions, weather, and expectation (Granger 1978, 33).

Economists Dagum and Bianconcini also provide examples of seasonal factors: in winter, rates of construction and productivity are lower whereas retail sees a seasonal peak. These scholars also emphasize the importance of analyzing both seasonally adjusted and unadjusted data:

Because seasonality ultimately results mainly from noneconomic forces (climatic and institutional factors), external to the economic system, its impact on the economy as a whole cannot be modified in a short period of time. Therefore, it is of interest to decision makers to have the seasonal variations removed from the original series to obtain a seasonally adjusted series. Seasonal adjustment means the removal of seasonal variations in the original series jointly with trading day variations and moving holiday effects. The main reason for seasonal adjustment is the need of standardizing socioeconomic series because seasonality affects them with different timing and intensity (Dagum and Bianconcini 2016, 64)

As I created data visualizations out of the import and export statistics, I realized the importance of seeing the seasonality and the variations across years and decades. Despite this, I also recognize that plotting and comparing the annual sums provides a holistic understanding of the trade relationship. Thus, I utilize both sets of data in my research in an effort to be wholly transparent.