This is really the whole story about cycles. - You can predict that part of the traffic that comes in regularly recurring bunches. You cannot predict by your 10- or 20-minute cycles the crowd of people that will come along when the feature of the local movie house is over. Your predictions are only partial. But they are good as far as they go if the cycles keep on coming true, and if you have timed them right. Moreover, by finding something else (in the example given, a bus schedule) with regularly recurring cycles of the same length you get a clue as to possible cause and effect and relationship - But to make sure, you need to run your clue down. Where you have partial regularity you have partial predictability. For example, suppose you have a chart recording your heart beats over the period of a minute. Now look at the first half of it only. If you count 30 beats in the 30 seconds You have under examination you will not be far wrong if you forecast another beat at 31 seconds, another at 32 seconds, etc.
Perhaps, if your heart speeded up a little, you would be quite wrong if you made your projection ten beats ahead, but by making your forecast only a beat at a time and revising your forecast each beat, you could probably make your prediction come out fairly well.
Another example of partial predictability: Suppose you knew that February 1st is, in New York City, on the average, the coldest day of the year. Suppose, on the basis of this information, you forecast February 1, 1952 as the coldest day of next winter. It is almost certainly true that February 1st will be colder than August 1st, but in any given year it may not be the very coldest day by as much as one or two months. The cycle of the year influences the weather but it does not completely dominate it. From a knowledge of the cycle of the year alone you could forecast the coldest day only with wide tolerances, yet the cycle of the year is very real.
Almost everything fluctuates with rhythm - that is, in more or less regular cycles. Putting it another way, almost everything acts as if it were influenced by regularly alternating up and down forces which first speed it up and then slow it down.
Random Ups and Downs Too
Everybody would know this fact were it not for two things: First, in addition to the cycles there are accidental (random) fluctuations in things, too. These randoms hide the regularities so that at first glance you do not see them. Second, things act as if they were influenced simultaneously by several different rhythmic forces, the composite effect of which is not regular at all.
If we had several moons, the ups and. downs of the tides would be very irregular. All the other moons would mix things up. It might have taken us much longer to find out about the tides.
Separating Cycles Easy
If you have a long enough series of figures with which to work it is not too hard to separate the different regular cycles from each other. When this has been done, you can project each regular cycle into the future - Then you can easily find out the combined or composite future effect of all the various cycles. When you have done this you have a preview of what is going to happen (a) if the cycles continue, and (b) except as the cycles may be upset or distorted by accidental random non-cyclic events.
Why wouldn't the cycles continue?" you may ask.
I'll give you one reason: The cycles may have been present in the figures you have been studying merely by chance. The ups and downs you have noticed which come at more or less regular time intervals may have just happened to come that way. The regularity - the cycle - is there all right, but in such circumstances it has no significance. If you are zealous enough you can find regularity in almost anything, including random numbers where you know that the regularity has no significance and know it will not continue.
Many Repetitions Needed
How can you tell in any given instance whether or not the regularity you see is the result of a real underlying cyclic force which will continue to fluctuate regularly in the future?
The answer is, if the cycle has repeated enough times with enough regularity and with enough dominance, the chances are that it is the result of real cyclic forces.
Let me give you an example: Pick up a pack of playing cards and start to deal. The first card is red, the second is black, the third is red, the fourth is black. You have two waves of a regular cycle, red, black, red, black. But this sequence could easily come about by chance. You continue to deal: red, black, red, black. Four times in a row now, this regular alternation. It could still be chance, but it couldn't be chance very often. Continue to deal. Red , black, red, black, red, black. Seven times now! It could still be chance, but it is less and less likely. It begins to look as if somebody had stacked the cards. You go through the entire deck.
Twenty-six times! "Somebody certainly stacked the deck," you say. "It couldn't happen this way by chance once in a million."
Less and Less Likelihood of Chance
Exactly the same sort of reasoning applies to the cycles you see in the ups and downs of the stock market, or the sales of your own company, or the weather, or anything else in which you may be interested. The more the cycle has dominated, the more regular it is, and the more times it has repeated, the more likely it is to be the result of a real cyclic force that will continue. If it has not dominated enough, or has not been regular enough, you must have more repetitions to get equal assurance.
Well then, supposing that there are these rhythmic cycles in something. Suppose further that you have some knowledge of cycles. So what?
By using nothing more complicated than simple arithmetic you can find the cycles. By seeing how many times each cycle has repeated in the past you can have a pretty fair idea of its significance (i.e. whether or not it will continue). And by projecting all the significant cycles into the future you can get some light on what is ahead insofar as the cycles govern.
Cycle Forecast Like a Weather Forecast
In weather we are used to forecasts in terms of probabilities. "The probability for the Pittsburgh area is for snow." When you hear such a forecast on the radio you don't rush out to put on your chains. The weather man may be wrong. You wait for the snow to fall. Then you put on your chains. But the forecast warns you that snow is likely, and on the strength of this fact you do make sure that you have your chains with you. If you are to make use of cycles in your business or your stock market forecasting you are going to have to use the same approach. Moreover, just as you refrain from shooting the weather man when he is wrong, I hope you will refrain from shooting the cycle analyst too.
Cycles are not the whole answer, but on the other hand they are indispensable in attempting to arrive at the whole answer.
Cycles remind me of women. Women are not perfect (with individual exceptions, I hasten to add). But they are the best thing so far invented for the purpose. Until something better comes along we will have to make shift with them as they are - or else miss the values they have to offer.
Let's find out all we can about them!
Cycles Show Us Our Ignorance
The second reason that the study of cycles is important arises from the fact that wherever you have rhythmic variation - or for that matter pattern of any sort - you probably have a cause. For example, if you scatter iron filings on my desk you would expect them to be distributed at random over its surface. If, to the contrary, you find them arranging themselves into a pattern, you have a right to assume that some unknown force is present - perhaps a powerful magnet in my top drawer.
Similarly, if you find pattern, or more specifically rhythm (reasonably regular cycles), in the alternate thickness and thinness of tree rings or rock strata, or in the abundance of insects, or in the prices of common stocks, you may be sure that there is a cause for such behavior. Also you may be sure that if you do not know that cause you do not fully understand the behavior with which you have to deal. Thus the study of cycles reveals to us our ignorance, and is therefore very disturbing to people whose ideas are crystallized. "If there are regularly recurring ups and downs in business or in prices, all I have ever learned is wrong" an eminent economist once told me; and he added, in a moment of unusual candor, "I simply cannot afford to accept such an idea. All my life's work would be ruined." Many doctors in the days of Pasteur must have felt much the same way about germs.
Identical Cycles Suggest Interrelationship
The study of cycles has a third value. Such study can indicate possible cause-and-effect relationships, thus helping to solve the very problems it reveals.
For example, when I was a boy I noticed that the moon came up at about an hour later each night. When I visited the seashore I noticed also that the tide came in half an hour or so later each twelve hours. I did not have the wit to go on from there to note that two consecutive tides were delayed by exactly the same amount of time as was the moon, and therefore that there was perhaps some connection between the two. I did not have the wit to make this observation, I say, but one of the older astronomers did. In doing so he demonstrated the third value of a knowledge of cycles: Where the rhythms in two different phenomena have the same average time span, you are justified in suspecting a possible direct or indirect interrelationship between the phenomena.
For these three reasons many scientists are interested in the study of cycles.
First, it is the business of science to predict; second, it is the business of science to solve mysteries and to learn the "how" of things; and third, the true scientist welcomes any tool that gives him hints as to possible cause and effect relationships. Perhaps 3,000 scientists, the world over, have concerned themselves with the subject, and have written scientific papers embodying the results of their observations.
Scientists however, like all the rest of us, are pretty self centered. Thus the mammalogist interested in cycles in animal abundance is usually not much interested in geological cycles, or in astronomical cycles, or in business cycles. He is primarily, and usually exclusively, a mammalogist. Likewise the men in each of perhaps thirty different aspects of natural and social science are likewise specialists, each involved in his own little field.
However, the techniques of cycle analysis are the same whether one is studying biological data or financial data. And - much more important - it is only by studying cycles in all sorts of phenomena, and finding which phenomena have cycles of identical average time span, that we are likely to get hints of cause and effect relationships.