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.
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