EXECUTIVE SUMMARY
- A simple neural net (NN) is created to predict in advance California
monthly UFO sightings as per NUFORC database. Some adjustments were
made to allow for the intro of a web report form in approx March 2006.
- The input data are planetary positions as per an ephemeris nominally
tuned to 2000-2050 but supposedly of reasonable accuracy back to
1950. Other data include surface temperature measures from ground
stations and satellites (the JMA gridded monthly dataset).
- The model is able to predict monthly California UFO sightings 12m in
advance to an accuracy of around +-2. In particular, the NN is
trained to predict "outliers" or UFO sighting clusters in preference
to the mundane seasonal month-to-month average counts. But it seems to
perform adequately for all months in the data.
- Similar models built to predict sightings in other US states were
not always as successful although many were. It seems some US states
generate sightings similar to "white noise". This does not mean
those data are "junk" and all California sightings are "something".
The model is not built to decide whether a particular predicted
sighting is the result of "weather", "alcoholic haze", or "unusual
physical object".
- Further work is in train to produce an annual or monthly UFO
prediction s/w that might be useful to pre-position or at least
anticipate UFO reports from certain regions up to 12m in advance.


This article is a outline of a very simple neural network that
predicts UFO sightings from sat-based surface temperature data and an
ephemeris of various planetary positions.

These data were not chosen because they are the best correlates of UFO sightings data, but because they are reliably curated and updated by
govt agencies around the world and will provide a continuing quality
data stream into the future. The ephemeris data is generated by a
program distilled from tabulated data that can also be updated into
the future and also can be zoomed into hour by hour resolution if
necessary. Supposedly the Dec and RA for the planets in the dataset
are accurate to within a couple of arc minutes. Adequate for our
purposes here.

The s/w -- simulating a simple "brain" about the size of a sea squirt
(~200 neurons) -- takes a large set of monthly time series from the
online datasets and is trained to predict UFO sightings for California
from 1950-present as given by the NUFORC database. The present set-up
predicts sightings 12 months into the future. I.e. given data upto the
end of 2020 we can use the s/w to predict UFO activity in California
upto the end of 2021.

It turns out these sightings can be predicted to high precision given
they are only month by month resolution. The training procedure finds
the final error is within +-2 sightings for each month. In particular
the error band applies to many of the "flaps" or sightings clusters
that have been seen in Cal from 1950.

We can not decide just here whether the "weather data" or the "planet
data" has a larger influence on the accuracy of the predictions. In
any case, simply building a "good predictor" can't tell you who is
operating the UFO objects (or whatever) in question or whether some
large fraction of the sightings are e.g. weather-induced
hallucinations of some kind.

But it quite interesting the input data chosen can predict the
"outliers" so well.

Here's a sample of the "observed" versus "predicted" California
sightings since 2000:

Date of data Adjusted(*) NUFORC sightings for California
for 1 year later
YYYY.MM OBS PRED
2000.04 44 44
2000.12 55 55
2000.21 44 44
2000.29 55 55
2000.38 44 44
2000.46 110 110
2000.54 110 109 <- prediction too low by 1
2000.62 55 55
2000.71 131 131
2000.79 66 65 <- 1
2000.88 55 54 <- 2
2000.96 55 55
2001.04 55 54
2001.12 44 44
2001.21 44 44
2001.29 22 22
2001.38 55 56
2001.46 88 88
2001.54 44 43
2001.62 88 88
2001.71 55 55
2001.79 44 44
2001.88 55 55
2001.96 22 21
2002.04 44 43
2002.12 22 21
2002.21 22 22
2002.29 55 55
2002.38 44 44
2002.46 66 66
2002.54 88 88
2002.62 66 66
2002.71 44 44
2002.79 44 43
2002.88 55 55
2002.96 55 55
2003.04 55 56
2003.12 22 21
2003.21 22 22
2003.29 110 109 <-- predicts cluster
2003.38 66 66
2003.46 153 153 <-- predicts cluster
2003.54 66 66
2003.62 22 22
2003.71 88 88
2003.79 88 88
2003.88 22 22
2003.96 22 22
2004.04 44 44
2004.12 22 21
2004.21 44 45
2004.29 88 88
2004.38 44 44
2004.46 131 131 <-- predicts cluster
2004.54 153 153 <-- predicts cluster
2004.62 66 66
2004.71 22 22
2004.79 66 65
2004.88 66 65
2004.96 55 55
2005.04 44 44
2005.12 131 131 <-- predicts cluster
2005.21 11 11
2005.29 33 33
2005.38 36 36
2005.46 34 34
2005.54 62 62
2005.62 52 52
2005.71 44 44
2005.79 44 44
2005.88 54 54
2005.96 66 66
2006.04 55 55
2006.12 31 31
2006.21 59 59
2006.29 36 36
2006.38 30 30
2006.46 46 47
2006.54 57 57
2006.62 50 50
2006.71 42 42
2006.79 48 48
2006.88 70 70
2006.96 58 58
2007.04 72 71
2007.12 52 52
2007.21 49 49
2007.29 59 59
2007.38 44 44
2007.46 65 65
2007.54 38 38
2007.62 54 54
2007.71 57 57
2007.79 77 77
2007.88 83 83
2007.96 50 50
2008.04 98 97
2008.12 61 60
2008.21 47 47
2008.29 30 30
2008.38 34 34
2008.46 55 55
2008.54 65 65
2008.62 66 66
2008.71 54 54
2008.79 42 42
2008.88 43 44
2008.96 44 44
2009.04 52 52
2009.12 30 30
2009.21 33 33
2009.29 38 38
2009.38 42 43
2009.46 42 42
2009.54 78 78
2009.62 63 63
2009.71 67 67
2009.79 52 51
2009.88 57 57
2009.96 41 41
2010.04 46 46
2010.12 54 53
2010.21 42 42
2010.29 52 51
2010.38 39 38
2010.46 37 37
2010.54 60 60
2010.62 70 70
2010.71 47 47
2010.79 55 55
2010.88 50 50
2010.96 65 66
2011.04 63 63
2011.12 35 34
2011.21 48 48
2011.29 57 57
2011.38 51 51
2011.46 79 79
2011.54 69 69
2011.62 75 75
2011.71 70 70
2011.79 73 73
2011.88 71 71
2011.96 60 59
2012.04 40 40
2012.12 45 45
2012.21 52 52
2012.29 53 53
2012.38 56 56
2012.46 58 58
2012.54 67 67
2012.62 64 64
2012.71 61 61
2012.79 57 57
2012.88 64 64
2012.96 102 102 <-- predicts cluster
2013.04 90 89
2013.12 73 72
2013.21 61 61
2013.29 77 77
2013.38 60 60
2013.46 80 80
2013.54 72 72
2013.62 79 79
2013.71 66 66
2013.79 68 67
2013.88 55 55
2013.96 67 67
2014.04 60 60
2014.12 39 40
2014.21 45 46
2014.29 34 34
2014.38 36 36
2014.46 34 34
2014.54 53 53
2014.62 48 48
2014.71 73 74
2014.79 72 73
2014.88 250 251 <-- predicts cluster
2014.96 50 51
2015.04 38 39
2015.12 64 65
2015.21 38 39
2015.29 34 34
2015.38 23 23
2015.46 49 50
2015.54 91 91
2015.62 51 51
2015.71 63 64
2015.79 40 41
2015.88 42 42
2015.96 36 36
2016.04 31 31
2016.12 34 34
2016.21 33 33
2016.29 51 51
2016.38 47 47
2016.46 37 37
2016.54 48 48
2016.62 46 46
2016.71 48 48
2016.79 58 58
2016.88 46 47
2016.96 95 95
2017.04 34 34
2017.12 42 42
2017.21 20 21
2017.29 16 16
2017.38 23 23
2017.46 10 11
2017.54 36 36
2017.62 32 32
2017.71 25 25
2017.79 44 44
2017.88 15 16
2017.96 17 18
2018.04 18 19
2018.12 23 23
2018.21 23 23
2018.29 27 27
2018.38 25 25
2018.46 37 37
2018.54 48 48
2018.62 39 39
2018.71 60 60
2018.79 68 68
2018.88 77 77
2018.96 48 48
2019.04 52 52
2019.12 71 70
2019.21 74 74
2019.29 98 99
2019.38 42 41
2019.46 37 37
2019.54 53 53
2019.62 47 47
2019.71 27 27
2019.79 30 29
2019.88 46 46
2019.96 42 42

(*) Data prior to around March 2006 was scaled by 9.8 to allow for the
creation of a web report form around then. The adjustment
minimizes the relevant F statistic to around 1.02 i.e. the
variance of the pre-2006 and post-2006 data is about equal.


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