When I was working on the financial audit of a charitable organization, I gained inspiration to look at the seasonal dimension of data. The organization showed me how they had been tracking month-to-month contributions throughout the year. The organization’s contributions would spike at Christmas time, because that’s when people give the most money to charities. Most of the contributions for the year would come during December, and the money would have to hold out for several months. They had recorded the accounting information in a database, which they would use to produce a series of bar graphs, one for each year, which they were able to display all in one picture, with each year of bars standing in front of the prior year’s row of bars. The following chart represents the monthly contributions to a charitable organization over the course of ten years. The organization is hypothetical, and the data is produced using a random number generating algorithm. The same chart could just as easily represent the sales of a retail business.
The resulting chart that the organization used amounted to a three dimensional surface plot that compared each year’s seasonal giving. In this way, the organization was able to determine visually if seasonal giving was relatively high or low, or if the money had started to come in early or late. The same principal can be used for retail, which has exactly the same financial cycle. I kept the idea in the back of my mind for years until my question about local climate came up. After that, I didn’t want to look at seasonal data any other way.
This way of visualizing data can, and should be used for financial markets, because it provides insight into anomalous data points such as historic bubbles and crashes, in light of seasonal patterns.
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