Newsgroups: alt.ufo.reports,alt.paranet.ufo
From: kymhorsell@gmail.com
Subject: ufo weather -- predicting ufo sightings ahead of time

[uploaded 75 times; last 31/10/2024]

EXECUTIVE SUMMARY:
- We've seen before sea surface temperatures seem to be predictive of
  UFO sightings across N Am.
- We can leverage that to create "weather forecasts" for UFO sightings
  7 or more days out. The model produced here is for 7d in advance.
- It is possible to produce reasonably accurate UFO forecasts for key
  regions across the US or forecasts broken down by object type.  They
  aren't always going to show up on radar so this might be the only
  way you'd know they were ever there. :)


In previous posts we looked at how the weather in key remote locations
-- a list that reads a bit like the top 10 paranormal sites -- seems
to predict UFO sightings across e.g. N Am.

While we previously tried to use that information to zero in on
locations that may be, if not UFO bases then maybe transit hubs, we
can also use it to predict UFO activity in the future; a kind of UFO
weather forecast 7, 14, and maybe more days out.

Some preliminary model-building indicates sea surface temperature may
predict UFO activity even 1 or 2 months in advance. Not too surprising
if we consider UFOs are phenomena intimately connected with the oceans
and probably "the deeps".

Here we will run a model-building AI to make a simple predictor of
daily UFO activity from daily satellite-based SST.

I'll use the daily maps produced by the AUS BoM and the "not Lights"
NUFORC daily sighting data. ("Light" type UFO's are presumed to
include a lot more junk than average given the growing list of things
that look like lights-in-the-sky. Not only Starlink but more and more
sophisticated drones and drone herds one of which was coordinated
enough I noticed to produce an image of a galloping reindeer across
the sky someplace last Xmas).

The s/w is a simple AI that uses its experience from past models and
what it has learned about the different datasets being combined.
While the models are sometimes "not good" because the s/w has
substituted its sometimes patchy knowledge for actually measuring
things via a statistical test it can also do better than a human
because such knowledge can short-circuit doing a huge amount of
processing which enables some unusual combinations of things to be
tried out instead -- sometimes things a human would never think to try
and which turns out to "make sense" in retrospect.

We divide the globe into 16 bands of latitude and 16 bands of
longitude and create time series of the avg sea surface temperature in
each box from Jan 2016 to the present. Using the NUFORC database we
obtain another time series of daily sightings less any sighting that
mentioned "light".

After "thinking" about the problem for a while and running quite a few
tests the s/w spits out the model:


 REWEIGHTED LEAST SQUARES BASED ON THE LMS
 *****************************************


     VARIABLE     COEFFICIENT    STAND. ERROR     T - VALUE     P - VALUE
   ----------------------------------------------------------------------
           x1         0.02940         0.00690       4.25789       0.00005
           x2         0.00621         0.00584       1.06357       0.29022
           x3         0.00661         0.00613       1.07823       0.28366
           x4         0.00194         0.00180       1.07889       0.28337
           x5         0.01554         0.00590       2.63307       0.00988
           x6         0.01297         0.00496       2.61200       0.01046
           x7         0.01337         0.00418       3.19658       0.00189
           x8         0.01266         0.00426       2.97339       0.00373
     CONSTANT         1.54986         0.26434       5.86316       0.00000

 WEIGHTED SUM OF SQUARES =        50.52532
 DEGREES OF FREEDOM      =        95
 SCALE ESTIMATE          =         0.72928
 COEFFICIENT OF DETERMINATION (R SQUARED) =        0.53985
 THE F-VALUE =       13.932 (WITH   8 AND   95 DF)   P - VALUE = 0.00000
 THERE ARE   104 POINTS WITH NON-ZERO WEIGHT. 
 AVERAGE WEIGHT          =         0.98113


x1 to x8 are the SST of different regions which appear in the map,
below.  Most of the locations are familiar from previous work with
similar models. We see the site selected as "x1" because it was the
most predictive variable for UFO sightings 7+ days into the future is
a region around S America that takes in part of the Pacific Coast as
well as Lake Titicaca. (It turns out -- surprise -- even the water
temp of Lk T itself is fairly predictive of future UFO activity across
N Am in the near future).

The model claims to predict about 53% of the day-to-day variation in
UFO sightings. This isn't too bad considering it's generally
considered only around 10% of all sightings -- maybe 20% of
not-lights-in-the-sky -- are "something".

It's possible the model is capturing the same "something" along with
an equal number of atmospheric phenomena that are strictly caused by weather.

The plot of UFO data versus the model is also given below. It shows
almost all peaks in the sightings curve are predicted by the model.
Remember this model is using *past* data to predict sightings.  
So the fit is even more remarkable than it superficially appears.

Given we have the SST data available day to day into the future a
model like this could predict how many sightings might be "something"
in coming days or weeks.  While this particular model is looking at
the "total" daily non-LITS sightings we can just as easily cook up
models for particular UFO types and/or for particular regions of N America.

Just in case anyone wants to get ready for possible visits and/or flaps.

The output from the model is available in graphic form at
<kym.massbus.org/UFO/model>. 

The full gore of the output from the PROGRESS stats package used by
the AI s/w to evaluate the models it's building is in
<.../foo.L49log38.152>.

The plot of which regions were used to make the predictions is given in 
<.../out.gif>. 

The plot of the model vs sightings ("observations") is in 
<.../oe.gif>.  

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