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Data Stream Momentum
Directionals
Root Beings
The Experiment

## 3.5 Conclusion

This Notebook has shown that while
the Traditional Mean Averages are good descriptors of Static Sets, they are
insufficient for describing Live Data Streams. The Mean Averages aren't
sensitive enough for a Live Stream. In their insensitivity, they don't
acknowledge Decay and they are not responsive enough to Fresh Data. Also the
Mean Average isn't relevant enough. A Live Data Stream wants an average that is
both sensitive and relevant. Enter the Decaying Average. We derive it and show
that it makes up for all the shortcomings of the Mean Average. It is Sensitive
to the Present and the Past, and is Relevant. We discover that neural networks
are a way of calculating the Decaying Average. We also find that computing
Decaying Averages confers an evolutionary advantage on the organism. This
average allows the organism to predict, which enables it to anticipate, which
assists in survival. We then looked at the difference between the Range of
Possibility and the Realm of Probability, discovering that being able to
compute the Realm of Probability also enhances survival because it allowed an
organism to conserve precious resources. In the search for the Realm of
Probability we came upon the arithmetic Average of Changes and the geometric
Standard Deviation. These measures were connected by the Exponent of Change,
which organisms established experientially. Finally we looked at our final
fundamental tool, the Directional. This computational tool is the Decaying
Average of the Directional Changes. This Notebook establishes the foundations
of the three computational tools that are essential when dealing with Live Data
Streams, i.e. Decaying Averages, Deviations & Directionals.