**Theme:** *A wrong turn in the mathematical modeling of time-series was taken almost a century ago. Today, Academia should engage in remediation to overcome the detrimental influence on the teaching and practice of time-series analysis in Science and Engineering.*

The objective of this page is to discuss the proper place in science and engineering of the fraction-of-time (FOT) probability model for time-series data, and to expose the resistance this proposed paradigm shift has met with from those indoctrinated in the theory of Stochastic processes, to the exclusion of the alternative FOT-probability theory. It is helpful to first consider the broader history of resistance to paradigm shifts in science and engineering. The viewer is therefore referred to Page 7, Notes on the Detrimental Influence of Human Nature on Scientific Progress, as a prerequisite for putting this page 3 in perspective.

The macroscopic world that our five senses experience—sight, hearing, smell, taste and touch—is analog: forces, locations of objects, sounds, smells, and so on change continuously in time and space. Such things varying in time and space can be mathematically modeled as functions of continuous time and space variables, and calculus can be used to analyze these mathematical functions. For this reason, developing an intuitive real-world understanding of analysis and, in particular here, spectral analysis of time-records of data from the physical world requires that continuous-time models and mathematics of continua be used.

Unfortunately, this is at odds with the technology that has been developed in the form of computer applications for carrying out mathematical analysis, calculating spectra, and associated tasks. This technology is based on discrete-time and discrete function-values, the numerical values of time samples of continuous measurements of phenomena. Therefore, in order for engineers, scientists, statisticians, and others to use the available computer tools for data analysis and processing at a deeper-than-superficial level, they must learn the discrete-time theory of the methods available—the algorithms implemented on the computer. The discreteness of the data values that computers process can be ignored in the basic theory of statistical spectral analysis until the question of accuracy of the data representations subjected to analysis and processing arises. Then, the number of discrete-amplitude values used to represent each time sample of the original analog data, which determines the number of bits in a digital word representing a data value, becomes of prime importance as does the numbers of time samples per second.

Consequently, essentially every treatment of the theory of spectral analysis and statistical spectral analysis available to today’s students of the subject presents a discrete-time theory. This theory must, in fact, be taught for obvious reasons but, from a pedagogical perspective, it is the Content Manager’s tenet for this website that the digital theory should be taught only after students have gained an intuitive real-world understanding of the principles of spectral analysis of analog data, both statistical and non-statistical. And this requires that the theory they learn be based on continuous-time mathematical models. This realization provides the motivation for the treatment presented at this website.

Certainly, for non-superficial understanding of the use of digital technology for time-series analysis, the discrete-time theory must be learned. But for even deeper understanding of the link between the physical phenomena being studied and the analysis and processing parameters available to the user of the digital technology, the continuous-time theory must also be learned. In fact, because of the additional layer of complexity introduced by the approximation of analog data with digital representations, which is not directly related to the principles of analog spectral analysis, an intuitive comprehension of the principles of spectral analysis, which are independent of the implementation technology, are more transparent and easier to grasp.

Similarly, the theory of statistical spectral analysis found in essentially every treatment available to today’s students is based on the stochastic-process model. This model is, for many if not most signal analysis and processing applications, unnecessarily abstract and forces a detachment of the theory from the real-world data to be analyzed or processed, except in the case of Monte Carlo simulations of data analysis or processing methods. To be sure, such simulations are extremely common and of considerable utility. But, for many applications in the various fields of science and engineering, there is only one record of real data; there is no ensemble of statistically independent random samples of data records. In such cases, knowing only a statistical theory of ensembles of data records (stochastic processes) is a serious impediment to intuitive real-world understanding of the principles of statistical spectral analysis of single records of time-series data.

For this reason, it is the Content Manager’s tenet that for the sake of pedagogy the discrete-time digital stochastic-process theory of statistical spectral analysis should be taught only after students have gained an intuitive real-world understanding of the principles of statistical spectral analysis of continuous-time analog non-stochastic data models. This avoids the considerable distractions of the nitty-gritty details of digital implementations and the equally distracting abstractions of stochastic processes. No one who is able to be scientific can successfully argue against this fact. The arguments that exist, and explain the other fact—that the theory and method of discrete-time digital spectral analysis of stochastic processes is essentially the exclusive choice of university professors and of instructors in industrial educational programs—are non-pedagogical. The arguments are based on economics—directly or indirectly: 1) the transition in philosophy that occurred along with first the electrical revolution and second the digital revolution (not to mention the space-technology revolution and the military/industrial revolution)—from truly academic education to vocational training in schools of engineering (and in other fields of study as well); 2) economic considerations in the standard degree programs in engineering (and other technical fields)—B.S., M.S., and Ph.D. degrees—limit the amount of course-work that can be required for each subject in a discipline; 3) economic considerations of the students studying engineering limit the numbers of courses they take that are beyond what is required for the degree they seek; motivations of the great majority of students are shortsighted and focused on immediate employability and highest pay rate, which are usually found at employers chasing the latest economic opportunity; 4) motivations of professors and industry instructors are affected by faculty-rating systems which are affected by university-rating systems: numbers of employable graduates produced each year reign, and industry defines “employability”. Capitalism typically values immediate productivity (vocational training) over long-range return on investment (education) in its employees. The problem with vocational training in the modern world is that the lifetime of utility of the vocation trained for today is over in ten years, give or take a few years. Industry can discard those vocationally-trained employees who peter out and hire a new batch.

In closing this argument for the pedagogy adopted for this website, the flaw in the argument “we don’t have time to teach both the non-stochastic and stochastic theories of statistical spectral analysis” is exposed, leaving no rational excuse for continuing with the poor pedagogy that we find today at essentially every place statistical spectral analysis is taught.

FACT: *Gaining an understanding of the relatively abstract stochastic-process theory of statistical spectral analysis is a trivial task* *once the down-to-earth probabilistic interpretation of the non-stochastic theory is understood*.

BASIS: The basis for this fact is that one can define all the members of the ensemble of time functions x(t, s), where *s* is the ensemble-member index for the stochastic process ** x**(

The WCM introduced a counterpart of Wold’s Isomorphism that achieves the same “trick” for cyclostationary stochastic processes and poly-cyclostationary stochastic processes [Bk1], [Bk2], [Bk3], [Bk5]. This is the subject of this page 3.