Now these are just a few examples
of the types of things that can be analyzed using time series. And just about anything that has a time
factor in it can be analyzed in this way. So what types of things can we do with
machine learning over time series? The first and most obvious is prediction
of forecasting based on the data. So for example with the birth and
death rate chart for Japan that we showed earlier. It would be very useful to predict future
values so that government agencies can plan for retirement, immigration and
other societal impacts of these trends. In some cases, you might also want
to project back into the past to see how we got to where we are now. This process is called imputation. Now maybe you want to get an idea for what
the data would have been like had you been able to collect it before
the data you already have. Or you might simply want to
fill in holes in your data for what data doesn't already exist. For example,
in our Moore's law chart from earlier. There was no data for some years because
there were no chips released in those years, and you can see the gaps here. But with imputation, we can fill them up. Additionally, time series prediction
can be used to detect anomalies. For example, in website logs so
that you could see potential denial of service attacks showing up as a spike
on the time series like this. The other option is to analyze the time
series to spot patterns in them that determine what
generated the series itself. A classic example of this is to analyze
sound waves to spot words in them which can be used as a neural network for
speech recognition. Here for example, you can see how
a sound wave is split into words. Using machine learning, it becomes
possible to train a neural network based on the time series to
recognize words or sub words.