By Nathaniel Hansen, M.A., COO, Foundation for The Study of Cycles
This article is about natural cycles, which are the basis of predictive analytics. Predictive Analytics as a discipline is at a critical time as we approach 2016, influencing an increasing number of players in various market segments. Gaining an understanding of the history of cycles methodology AND the technologies its founders are currently working to foment may be useful to predictive analytics practitioners.
First of all, what is a "cycle"? It is very simple. Here is an example:
Suppose you are visiting my house and, looking out of the window, notice a bus pass by at 10:00 a.m. Half an hour later at 10:30 you notice another bus pass. At 11:00 you see another one, "Ah ha!" you say. "Buses here run every thirty minutes." You have discovered a cycle all by yourself, without benefit of slide rule or gobbledygook. Cycles are just as simple as that.
And how are natural cycles related to predictive analytics?
Natural cycles are the cause of changes in all events - man-made or nature-driven. Predictive analytics looks squarely in the rear-view mirror to try to understand the path of the future. “Natural cycles” – properly used - can give the roadmap – of what was before – and is the inevitable course ahead. Humans can use an understanding of cycles to perform better predictive analytics.
These days there is a lot of conversation about predictive analytics. And many challenges as well, of course. With the launch of IBM Watson API into mainstream usage, for example, many businesses are experimenting with software that crunches large amounts of data in order to predict future outcomes. This is useful since people do not have the time to read through every bit of data now being produced. Melissa Di Donato, head of European Channels & ISV Programs at Salesforce.com has stated that "90% of the world's data was created in the last two years.. By 2020 there will be ten times the current amount of data, much of it unstructured." Micheal Jordan,IEEE Fellow Michael I. Jordan, Pehong Chen Distinguished Professor at the University of California, Berkeley, has said (about the predictive analytics & big data "problem"), "You can make lots of predictions, and there’s a good chance that you will occasionally solve some real interesting problems. But you will occasionally have some disastrously bad decisions. And you won’t know the difference a priori."
Understanding cycles methodology gives one a view into that "a priori" difference, which Jordan speaks of. Remember, "a priori" knowledge or justification is independent of experience, as with mathematics. Cycles predictive output can be classified as "non-chance rhythms" in data - therefore "a priori". Cycles methodology is an under-utilized key in solving the problems predictive analytics struggles with today -- particularly in the case of working with big data.
To understand why and how, let's go into the history of cycles methodology - back to Edward R. Dewey himself. Edward R. Dewey, the Head of the U.S. Census and Chief Economic Analyst for President Hoover during the “Great Depression”, is the progenitor of contemporary cycles theory. After the Great Depression, economic analyst Dewey was asked by U.S. President Herbert Hoover to discover causes of the economic downturn via statistical analysis. Dewey found that it’s not just the economy that rises and falls according to waves that resonate in predictable patterns. Everything in nature follows these same wave patterns.
Dewey investigated the “WHAT” of the Depression rather than the “WHY”. When Hoover gave Dewey the onerous assignment of understanding the Great Depression, Dewey found that standard methods of economics could not provide him with coherent and consistent answers and models. Having access to vast amount of information at the US Commerce Department, Dewey adopted a novel approach. Rather than concerning himself with whys and causes of depressions, he decided to study how economic and business cycles occurred. He decided, in other words, to study the what, not the why, and in doing so he discovered something very significant. He discovered that cycles, waves of behavior, existed in almost all aspects of human and economic life, from the prices of pig iron to human emotions themselves.
Dewey first discovered the idea of “cycle synchronies” after he left government office by reading an account of the Matamek Conference on Biological Cycles, held in 1931. He discovered that not only were cycles of snowshoe rabbit and lynx populations in Canada equal in length to cycles he had personally discovered in the economy and the stock market, but they topped and bottomed at nearly the same time!
While the influence of cycles methodology upon financial market related research dominates conversation in many circles, there are other areas in which the approach is being used as well. Quite literally, any endeavor that depends on the usage of materials over time periods could benefit from the application of cycles methodology. An example would be self-directed heating and cooling systems in large buildings and malls regulating temperatures based on projected fluctuations in outside temperatures.
Fast-forwarding to the present, and the advent of IoT, (Internet of Things), cycles methods could be used to regulate basic functions of intelligent or "smart" objects. In medical applications, an application or apparatus could be constructed to predict a critical patient’s pulse, diastolic and systolic parameters. The intelligent pre-allocation of resources by governments, cities, corporations and individuals could also be guided by cycles principles.
A recent article by Bryan Kramer highlights the use of predictive analytics and IBM's Watson to guide content planning. Kramer uploaded his data to Watson and received charts showing best times to contact his various audiences. The charts from Watson that Kramer posted follow the same recurring patterns that many cycles charts dictate, based upon natural phenomena. Kramer quotes Brian Moran, an SMB expert, on the power of predictive analytics, “Used properly, predictive analytics can be a huge boon to the marketing efforts of companies of all sizes, in every industry.”
David Perales, the CEO of the Foundation for the Study of Cycles, says, "I believe predictive analytics is essentially at the same stage of development as technical analysis was in the early 1970’s. In fact many of the “predictive” mathematical tools are identical to what has been used in stock market analysis since that time. Now we are ripe for a paradigm shift to achieve the goals set out for the predictive analytics niche, a shift in public awareness and in perfecting the tools."
Perales goes on to say, "At the FSC, we are deeply into software devlopment. We are interested in producing a 99% accurate tool for forecasting and we are currently around 66% accuracy. Our scientists and programmers are very active in ironing out the kinks and solving the bugs so that our software can be of use to any industry in predictive analytics activities."
At the Foundation, we are assembling technologies that bring Dewey's work to life for the current paradigm. Remember, Dewey was asked by President Hoover to deliver an answer regarding the Great Depression, a solution. Dewey's response was to gather the vast data in the US Gov't archives at that time and tell people what he saw. And what he saw, beyond all other phenomena, was the recurring cyclical nature of reality, the way in which things happen over and over again. He then set himself to find out when specific cycles would affect specific businesses, communities and individuals. His care for the world and humanity was very deep. He wanted his president, his nation, his community, his family and the individual people of the world to be safe. And so does the team at the Foundation for the Study of Cycles.
3. Babylon's banksters : the alchemy of deep physics, high finance and ancient religion : an essay concerning the relationships between aether physics, economics, astrology, alchemy, geomancy, ancient temples, and the politics of suppression. Author: Joseph P. Farrell
4. Bryan Kramer, 5 Ways to Become the MacGyver of Predictive Analytics.