so as I mentioned we are interested
could we aggregate these individual
level predictions into a model to
actually make accurate version level
predictions and so we call this paper
forecasting high tide because we are
interested in predicting when our winner
most people online in retweeting so that
we could spread our message
and this is actually really you know
kind of pushed by a motivated by a
question that Jim pinata who's an MBA
student the tenacity and which is can we
predict when a tweety is most likely to
get each we did so one thing the thing
about is that you want to determine win
a large proportion of your followers
will be acting in the near future so
this is showing four weeks worth of data
and it's showing that the the peak times
that a lot of people are active varies
not only based upon the day of the week
but also from week to week and there's
some spikes and times and things like
that that we need to be able to capture
that orange iterate as much as possible
right now the optimal time varies from
week day to day and week to week so the
question is can we do better than a
straight-up seasonality model no
seasonality model this case it does not
mean like spring fall whatever and
instead what we mean is just a
time-based pattern that seems to be the
same right and so we're going to compare
our application of individuals malware
we take all the calls of state models
and we aggregate their predictions to a
seasonality model and to what's known as
an area auto regressive model and every
other aggressive model takes our
seasonality model but also adds in a
local excitement factor based upon
recent activity in the near past
so here's the seasonality model sees
melon that was really easy you just look
at all the past behavior and you make
the you'd make the prediction that the
number of retweets you're going to see
in any particular time is an average of
all the past times you've seen for that
particular day and at that particular
time right so you just average all the
data out right so if you're you have all
the data laid on top of each other and
this line is the averages of those data
so that becomes your prediction a
particular time so if this was the case
and you wanted to say I want to tweet at
the optimal time you tweet like here on
the
Thursday right here on the Friday right
and so for Saturday's days right
the autoregressive adds the fact that
there's excitement so actually this peak
in our beta we know it's caused by this
actually happens to be the capture and
death of Osama bin Laden when there's a
lot of activity on Twitter around that
and so this is not necessarily something
you want to capture because I'm not sure
you know depending upon what year you're
what message you're interested in
broadcasting you might not want to be
highly involved in that conversation
right but you know maybe what if your
news organization for instance this
would be a peak you'd like to be able to
predict right in which case you you you
want to Neil the caption low collected
init process so the aggregate auto
regressive model starts with the
seasonality model the VIN model is a
residual of component based upon the
past history so you know you can think
about it's a straight line so that past
history and if that straight line looks
like it's trending up its going to add
something to that seasonality model
right
so the aggregation of individuals model
is very simple it's we take those
epsilon machines those possible state
models that we've created for each of
different users and we look at what
state they're currently in and what
speed they are currently in tells us
what the probability of them tweeting in
the near future is right and so then we
can aggregate those probabilities and
aggregating across all the probabilities
gives us the expected number of tweets
to see in the near future right so we
have two users there are 50% likely
tweet into the next time period then we
went out together we're expecting to see
one tweet right because that's that's
the expected number we still would see
in that space
so the question becomes using these
different methods how well can we
predict the world within say 10% of the
top
peak of the period right that we will
that the quantile P star will be then
we'll be using that quantile for a
particular time period or predicting
right so we the way you compare these
models a lot of times is you don't use a
straight up accuracy of prediction
instead because you have this this
threshold for varying which is how
likely you are to be at that topic like
you need to know what the number is that
you're adjusting course and use a
receiver operating characteristic curve
and what this shows is for each of those
threshold values what is your false
positive rate versus your p positive
rating right and so what what the
takeaway from this is is that the
aggregation of individuals model does
pretty well and we're comparing two data
sets here now really in 2011 and 2012
and as you can see the seasonality model
does pretty well in in 2011 doesn't do
as well in 2012 mainly because a lot of
the data is now out of date on a model
right whereas the aggregation of
individuals model does a much better job
of kind of adapting it also goes down in
performance but you know not terribly so
and the auto regressive model is
slightly better in that case but for a
low false positive rate the animation of
individual small does success was it
does surprisingly successfully well
right and in fact for the low false
positive rates what you want to see if
you're a marketer who is sending out a
lot of content right then you you
probably have a limited number of tweets
you want to send out per day because
although I keep I'm gonna start thinking
you're spamming them and so you want to
have a little false positive rate you
want to be pretty sure that your
predictions are right so even if that
means you give up some mere true
positives you'd like to have a low false
positive rate of those scenarios right
and so tweeting in this space is where
you want to be so in general we would
say probably the average individualist
model points of being the best model in
the affective spaces that were looking
at
so what does this all mean in conclusion
well what it means is that there's a new
way of thinking about building
agent-based modeling mad models you can
take machine learning in paroles i've
shown you one way you can cause it's
technology you could use decision trees
you could use Association so let's use
the number of different other ways and
you can combine them to train and
automatically train in a jiffy power now
I'm not saying that were the first ones
to ever do this right we were the first
ones to try and start to do this on a
regular basis right a lot of people it
doesn't have in it one ounce for a
particular process borchardt where our
goal here is eventually to build a set
of knowledge in methods around this to
try and really provide some tools for
people to do this constantly and I've
actually build them into something like
that level right now a lot of this code
is written in Python or other things
right and but there's some limitations
this method it requires high resolution
time series data right you need to have
a lot of being a for the cause of state
models were not for the machine learning
plus a base model to work but of a free
cause the state modeling investing your
solution you need high resolution data
in the future word we'd like to build a
software platform of the line changes
potentially thing about implementing on
a gpgpu architecture because if you're
not not the institution because the
execution of the model involves a lot of
agents interacting but building the
model could be implemented on GPGPU
because each of these time series could
be read by an individual
processor who is individually building
the console state
and the other thing is I've only shown
you like one step look at that now the
argument that
Cosmo and Jim and several others make is
that these models will be predictive as
far as possible into the future because
we can predict the next ever greater
than that stuff they're gonna do well
but we know it's going to degrade
someone right and so trying to get a
better understanding of how quickly that
degrades would be interesting to do okay
so that's a quick intro into one person
advanced topic that I didn't what about
CRA questions dig near the forum and fix