Time Series Data: Why Collect It? - Time Series Databases - page 7

In the winter of 1848, Maury sent one of his Wind and Current Charts to Captain Jackson,
who commanded a ship based out of Baltimore, Maryland. Captain Jackson became the first
person to try out the evidence-based route to Rio de Janeiro recommended by Maury's ana-
lysis. As a result, Captain Jackson was able to save 17 days on the outbound voyage com-
pared to earlier sailing times of around 55 days, and even more on the return trip. When
Jackson's ship returned more than a month early, news spread fast, and Maury's charts were
quickly in great demand. The benefits to be gained from data mining of the painstakingly ob-
served, recorded, and extracted time series data became obvious.
Maury's charts also played a role in setting a world record for the fastest sailing passage
from New York to San Francisco by the clipper ship Flying Cloud in 1853, a record that las-
ted for over a hundred years. Of note and surprising at the time was the fact that the navigat-
or on this voyage was a woman: Eleanor Creesy, the wife of the ship's captain and an expert
in astronomy, ocean currents, weather, and data-driven decisions.
Where did crowdsourcing and open source come in? Not only did Maury use existing ship's
logs, he encouraged the collection of more regular and systematic time series data by creat-
ing a template known as the “Abstract Log for the Use of American Navigators.” The log-
book entry shown in Figure 1-3 is an example of such an abstract log. Maury's abstract log
included detailed data collection instructions and a form on which specific measurements
could be recorded in a standardized way. The data to be recorded included date, latitude and
longitude (at noon), currents, magnetic variation, and hourly measurements of ship's speed,
course, temperature of air and water, and general wind direction, and any remarks considered
to be potentially useful for other ocean navigators. Completing such abstract logs was the
price a captain or navigator had to pay in order to receive Maury's charts. [ 2 ]
Time Series Data Sets Reveal Trends
One of the ways that time series data can be useful is to help recognize patterns or a trend.
Knowing the value of a specific parameter at the current time is quite different than the abil-
ity to observe its behavior over a long time interval. Take the example of measuring the con-
centration of some atmospheric component of interest. You may, for instance, be concerned
about today's ozone level or the level for some particulate contaminant, especially if you
have asthma or are planning an outdoor activity. In that case, just knowing the current day's
value may be all you need in order to decide what precautions you want to take that day.
This situation is very different from what you can discover if you make many such measure-
ments and record them as a function of the time they were made. Such a time series dataset