The amount of new information is constantly increasing, faster than our
ability to fully interpret and utilize it to improve human experiences.
Addressing this asymmetry requires novel and revolutionary scientific
methods and effective human and artificial intelligence interfaces. By
lifting the concept of time from a positive real number to a 2D complex
time (kime), this book uncovers a connection between artificial
intelligence (AI), data science, and quantum mechanics. It proposes a
new mathematical foundation for data science based on raising the 4D
spacetime to a higher dimension where longitudinal data (e.g.,
time-series) are represented as manifolds (e.g., kime-surfaces). This
new framework enables the development of innovative data science
analytical methods for model-based and model-free scientific inference,
derived computed phenotyping, and statistical forecasting. The book
provides a transdisciplinary bridge and a pragmatic mechanism to
translate quantum mechanical principles, such as particles and
wavefunctions, into data science concepts, such as datum and
inference-functions. It includes many open mathematical problems that
still need to be solved, technological challenges that need to be
tackled, and computational statistics algorithms that have to be fully
developed and validated. Spacekime analytics provide mechanisms to
effectively handle, process, and interpret large, heterogeneous, and
continuously-tracked digital information from multiple sources. The
authors propose computational methods, probability model-based
techniques, and analytical strategies to estimate, approximate, or
simulate the complex time phases (kime directions). This allows
transforming time-varying data, such as time-series observations, into
higher-dimensional manifolds representing complex-valued and
kime-indexed surfaces (kime-surfaces). The book includes many
illustrations of model-based and model-free spacekime analytic
techniques applied to economic forecasting, identification of functional
brain activation, and high-dimensional cohort phenotyping. Specific
case-study examples include unsupervised clustering using the Michigan
Consumer Sentiment Index (MCSI), model-based inference using functional
magnetic resonance imaging (fMRI) data, and model-free inference using
the UK Biobank data archive. The material includes mathematical,
inferential, computational, and philosophical topics such as Heisenberg
uncertainty principle and alternative approaches to large sample theory,
where a few spacetime observations can be amplified by a series of
derived, estimated, or simulated kime-phases. The authors extend
Newton-Leibniz calculus of integration and differentiation to the
spacekime manifold and discuss possible solutions to some of the
"problems of time". The coverage also includes 5D spacekime formulations
of classical 4D spacetime mathematical equations describing natural laws
of physics, as well as, statistical articulation of spacekime analytics
in a Bayesian inference framework. The steady increase of the volume and
complexity of observed and recorded digital information drives the
urgent need to develop novel data analytical strategies. Spacekime
analytics represents one new data-analytic approach, which provides a
mechanism to understand compound phenomena that are observed as
multiplex longitudinal processes and computationally tracked by proxy
measures. This book may be of interest to academic scholars, graduate
students, postdoctoral fellows, artificial intelligence and machine
learning engineers, biostatisticians, econometricians, and data
analysts. Some of the material may also resonate with philosophers,
futurists, astrophysicists, space industry technicians, biomedical
researchers, health practitioners, and the general public.
