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<p>Knowingly or unknowingly, digital-data is an
integral part of our day-to-day lives. Realistically, there is
probably not a single day when we do not encounter some
form of digital-data. Typically, data originates from diverse
sources in various formats out of which time-series is a
special kind of data that captures the information about
the time-evolution of a system under observation. How-
ever, capturing the temporal-information in the context of
data-analysis is a highly non-trivial challenge. Discrete
Fourier-Transform is one of the most widely used methods
that capture the very essence of time-series data. While
this nearly 200-year-old mathematical transform, survived
the test of time, however, the nature of real-world data
sources violates some of the intrinsic properties presumed
to be present to be able to be processed by DFT. Adhoc
noise and outliers fundamentally alter the true signature
of the frequency domain behavior of the signal of interest
and as a result, the frequency-domain representation gets
corrupted as well. We demonstrate that the application
of traditional digital filters as is, may not often reveal
an accurate description of the pristine time-series characteristics of the system under study. In this work, we
analyze the issues of DFT with real-world data as well as
propose a method to address it by taking advantage of
insights from modern data-science techniques and particularly our previous work SOCKS. Our results reveal that
a dramatic, never-before-seen improvement is possible by
re-imagining DFT in the context of real-world data with
appropriate curation protocols. We argue that our proposed transformation DFT21 would revolutionize the digital
world in terms of accuracy, reliability, and information
retrievability from raw-data. </p>
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