EXECUTIVE SUMMARY
- We look at models predicting humanoid sightings from a particular dataset.
- The best individual predictors of humanoid activity are very much
as we've seen before for UFO's. The positions of the outer planets,
cosmic rays, and storm activity over the oceans.
- But a new method based on collecting similar data series into
"themes" produces a clearer picture. The theme "oceans" that
collect together many data series on ocean temperature, salinity,
pH, sea level, etc is overall the best explanation of humanoid
sightings. It seems humanoids are "associated" with the oceans more
than the land, surface weather, cosmic rays, or planetary positions.
- We also find themes connected with planets collectively or
individually also explain humanoid sightings -- just not as well as
ocean conditions.
- We also find adding UFO's to many themes improves their predictive
power. IOW UFO's have something to do with humanoid sightings, but
are not likely the "proximate cause".
- It seems "humanoids" are if not products of Earth evolution, are
apparently resident here. The oceans at depths 100-1000m show as the
biggest contributor to predicting humanoid sightings.


While we've seen in various mini-studies I've posted that UFO activity
seems to strongly correlate with both weather conditions in some
remote regions of planet Earth as well as the orbital positions of
certain key planets, notably the outer planets, we haven't looked
directly at the (reputed) pilots of these craft.

While somewhat less comprehensively documented, various types of odd
lifeforms or "alien" have been reported over the years and some people
have tracked these reports down and tried to document "all" sightings
from one or other decade since UFO's were declared a modern thing
sometime after WWII.

While some of these "catalogs" contain a lot of information, we'll
just use the dates of reports here to try and track down what other
data seems to predict the appearance of ALF's.

I'll take the data from "HUMCAT", Webb and Bloecher's dataset
available via CUFOS among others, that covers the period 1955 to 1978
and lists 421 "humanoid" type lifeforms reported from around the world.

Just converting to counts for each month over the period the data
becomes quite compact:

1955.38 1 1963.79 1 1970.04 2 1970.12 2 1970.29 1 1970.46 6 1970.62 2
1970.88 1 1971.04 3 1971.12 1 1971.21 1 1971.29 7 1971.38 2 1971.46 3
1971.54 3 1971.62 4 1971.71 2 1971.79 1 1971.88 2 1972.04 1 1972.21 7
1972.29 4 1972.38 1 1972.46 3 1972.54 3 1972.62 6 1972.71 2 1972.79 2
1972.88 4 1972.96 4 1973.04 2 1973.12 4 1973.21 4 1973.29 8 1973.38 8
1973.46 4 1973.54 2 1973.62 4 1973.71 3 1973.79 10 1973.88 4 1973.96 1
1974.04 4 1974.12 4 1974.21 9 1974.29 12 1974.38 4 1974.46 6 1974.54 6
1974.62 6 1974.71 3 1974.79 5 1974.88 7 1974.96 4 1975.04 4 1975.12 1
1975.21 12 1975.29 10 1975.38 5 1975.46 6 1975.54 11 1975.62 7 1975.71 5 1975.79 13 1975.88 5 1975.96 6 1976.04 5 1976.12 8 1976.21 7 1976.29 5
1976.38 9 1976.46 9 1976.54 9 1976.62 11 1976.71 10 1976.79 5 1976.88 2
1976.96 2 1977.12 4 1977.21 1 1977.29 4 1977.38 1 1977.54 4 1977.62 1
1977.71 3 1977.79 3 1977.88 1 1978.04 4 1978.12 1 1978.21 7 1978.29 5
1978.38 1 1978.46 1 1978.54 2 1978.62 3 1978.79 1 1978.96 1

My A/I software has in the past performed a simple analysis based on
trying to find another dataset that "looks like" a target dataset. It
tries very hard to make all comparisons statistically robust enough we
can be confident any small subset of them can assumed not to be just
due to a lucky association in the data.

The "top 10" list of these models by their explanation powers (R2) are
as follows:

Suspect Lag Filter R2
(m) (sd's)
gavufo-grey 0 1.5 0.66441174
gavhail-NV 0 1.5 0.62873867
gavsaturn-latecl 4 1.5 0.54717227
gavufo-Formation 12 1.5 0.53130921
presseg-150 6 1.5 0.50912298
presseg-180 4 1.5 0.49406649
gavpresband60 1 1.5 0.48112653
gavhail-WA 12 1.5 0.47581929
gavwind-WA 6 1.5 0.46847861
gavjupiter-Dec 12 1.5 0.46692107

The first column lists the "suspect" or "x" variable in a regression
against the HUMCAT data, above. The comparison first de-seasonalizes
and de-trends the HUMCAT data so a suspect variable wont simply match
up because it has the same kind of year-to-year trend or
month-to-month cycle as may be in the HUMCAT data.

The Lag column shows how much delay seems to find the optimal
match-up. E.g. the first line says "grey UFO" sightings explain 66% of
humanoid reports in the era in the same month (zero lag). But
"Formation" type UFO's also explain 53% of humanoid sightings if
lagged by 12m. Something about Formations means next year same time a
humanoid is generally reported somewhere.

The Filter column shows what% of datapoints were ignored to get
the best match. All match-ups here trimmed out any data more than 1.5
standard deviations from the trend-line. This means around 15% of data
might be trimmed off to make the match better. We don't want to simply
ignore too much of the data, but it's SOP to ignore 10-20% that might
be just "noise" aka mistakes or hoaxes or anything else that seems to
be very much different from the bulk of the data.

So, in summary of the above list, it seems UFO sightings, positions of
key planets (Saturn and Jupiter here), and various types of storm
activity between them explain an awful lot of humanoid
sightings. There is definitely (according to the stat tests) SOME
connection between each and humanoid sightings, and the R2 shows
roughly what fraction of humanoid sightings is linked with each
suspect variate.

But the AI s/w has now moved past using individual variables to match
against target datasets -- it now can cook up more complex models that
involve many datasets with the same "theme". It's hoped explaining
target datasets by themes instead of individual data series will make
the modeling even more robust and more understandable to people.

So the top "themes" and their explanation powers for HUMCAT data (with
those same preliminary modifications to remove seasonality and trend)
are as follows:


Theme R2
ocean 0.43880
ph 0.40985
dep100. 0.39034
jma 0.36092
dep10. 0.35435
wind 0.35285
sal 0.34663
poles 0.33158
storm 0.32298
hail 0.28126
dep1000. 0.27207
cosmic 0.27089
lat 0.26897
torn 0.24854
ecl 0.23843
saturn 0.23663
pres 0.23025
ant 0.22680
tmp 0.22123
seg 0.21743
dep1. 0.21053
-r 0.19919
jupiter 0.16933
ufo 0.16773
arc 0.16591
lon 0.16304
msl 0.15262
pre 0.14751
uranus 0.14719
neptune 0.13745
sunmoon 0.10419

In this run the s/w finds the "ocean" there allowed the best
predictive models to be created. The temperatures of various chunks of
ocean (usu divided into 10x10 degree grids down to 1000m) can be
combined to predict at least 44% of humanoid sightings. The "at least"
part is important. The s/w uses several different methods to build the predictive models and it uses the minimum R2 from all the methods to
estimate the explanation power of the theme. IOW the R2 here are not
directly comparable with the R2 in the section, above, where we looked
at associations of single data series only.

So it seems the "best" theme the s/w can find relates humanoid comings
and goings to changes in the ocean. The best explanation is not the
position of some planet or sunspots, cosmic rays, or anything else
that seems related to something outside the Earth.

Looking down the list we see e.g. the "cosmic" theme (from the various estimates of cosmic rays from various locations around the world since
the ~1950s) only explains around 27% of humanoid sightings -- about
1/2 as good as ocean temperatures.

The "ecl" and "saturn" theme -- to do with the ecliptic coordinates of
all the planets and the position of saturn, respectively -- come in
even less than cosmic rays. These things "are" connected with humanoid sightings but are much less relevant than ocean temperatures and the
position of planets seems even less important than cosmic rays in
understanding where and when humanoids might turn up.

Way down at the bottom of this list are the themes "sunmoon" and
"neptune" which relate to models build from the position data for the
sun+moon and neptune, resp. Even less important than "saturn" or "all
planetary locations".

OTOH we look at the list again and see "ph" explaining around 41% of
humanoid sightings. This is the recorded pH of various chunks of
ocean down to 1000m. It seems it isn't just ocean *temperatures* that
predict humanoid sightings. Combinations of seawater pH predict
almost as well. "Sal" further down the list is the sweater salinity
for various chunks of ocean. This also explains 35% of humanoids, much
better than the positions of any single or combination of planets.

We can also zero in what *part* of the ocean we seem to be looking
for. The themes "depNNN" look at the temperatures of various chunks
of ocean at various depths. "dep100" is the 100-1000m level. It seems
this is the ocean depth most connected with humanoid sightings. The
other themes dep1, dep100, dep1000 have a smaller R2. They are
related, but much less predictive.

Even without understanding fully what the other themes are and how
they may overlap, we can see the overall finding seems to be sightings
of humanoids are best predicted by ocean temperatures mostly around
the 100-1000m levels, and only very modestly by the position of
planets, the conditions at "the poles", the Arctic, Antarctic or ocean
storms or other surface weather.

It seems while some UFO's may be very tightly connected with some of
the other planets -- perhaps some types even originate from the moons
of Saturn, Neptune or Uranus -- sightings of humanoids seem to be
related to UFO's too, but are "mostly" related to the oceans of the
Earth. Sounds like they live here and are either close relatives or
originated here.

Finally, we can try to gauge how likely it is humanoid sightings have *something* to do with UFO's by trying to add themes together. If
the oceans explain a good chunk of humanoid sightings, if we create a
theme "ocean+ufo" can we build models that are even more predictive
than either "ocean" or "ufo" alone?

Here are the list of "multi-theme" models the AI tried:

Multitheme R2
oceanufo 0.47660
phufo 0.44497
dep100ufo 0.41113
windufo 0.35502
polesufo 0.32599
cosmicufo 0.31232
dep1000ufo 0.30943
preufo 0.26210
-rufo 0.24793
lonufo 0.22821
uranusufo 0.22507
latworldufo 0.21907
graceufo 0.14654
dep10ufo 0.14411

It seems adding ocean and ufo together results in models that explain
at least 48% of humanoid sightings -- the best result yet. Several
other multi-themes explain more than the base themes alone, too.

It seems UFO *are* connected in some way with humanoid sightings, but
the link is likely indirect. Humanoids doing come to Earth via a UFO
from Saturn, but maybe humanoid activity is stirred up a bit by a
visit from the neighbours.

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--- SoupGate-Win32 v1.05
* Origin: www.darkrealms.ca (1:229/2)