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.