Welcome to Practical Time Series Analysis! Many of us are "accidental" data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training. This course is designed for people with some technical competencies who would like more than a "cookbook" approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics. In practical Time Series Analysis we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more. We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future. Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language. It is a professional environment and fairly easy to learn. You can discuss material from the course with your fellow learners. Please take a moment to introduce yourself! Time Series Analysis can take effort to learn- we have tried to present those ideas that are "mission critical" in a way where you understand enough of the math to fell satisfied while also being immediately productive. We hope you enjoy the class!
Partial Autocorrelation and the PACF - Concept Development
Loading...
Del curso dictado por The State University of New York
Practical Time Series Analysis
169 calificaciones
De la lección
Week 4: AR(p) processes, Yule-Walker equations, PACF
In this week, partial autocorrelation is introduced. We work more on Yule-Walker equations, and apply what we have learned so far to few real-world datasets.
Conoce a los instructores
Tural SadigovLecturer
Applied Mathematics
William ThistletonAssociate Professor
Applied Mathematics
Welcome back to Practical Time Series Analysis.
In this lecture, we review the partial autocorrelation concepts, in effect,
we try to understand it just how it's calculated.
We've seen that for an ERP process,
the PACF can be a really good tool to tell us the order of the process.
We look at the PACF and
we determine when the spikes essentially die down into noise.
We'd like to know just what is being measured, however.
After this lecture, you'll be able to, in a regression sense, and
we'll apply that to time series, you'll be able to partial out a variable, and
you'll be able to describe to a friend or colleague what the PACF measures.
This very nice example available in several text books and
also some of the our packages, having to do with body fat.
To measure body fat is a pretty expensive and laborious process involving
people getting into big vast of water, etc., and looking at their displacement.
It would be nice if there was a simple, fast,
cheap, way, non-invasive way, to get the same kind of measurement.
And so, what this data set explores is,
why is it measuring somethings with essentially a caliper and a tape measure.
Triceps skinfold thickness, thigh circumference, and
mid-arm circumference to see if those would be good proxies,
if we could build the good regression model for
body fat based upon these simple, easy to measure variables.
If you look at the data set, the results look rather promising.
If you want, you may have access to these data sets somewhere else but
I'm going to show you that you can get it through the isdals library.
We'll just bring the body fat data set into play.
I like to always attach, so I can just call the variables directly.
And, in order to run the pairs command, in order to look at graphs of one on one
plots, we'll put the variables into a matrix and then we were in pairs.
You can see the triceps, its really a decent predictor of fat, so is thigh.
Thigh is a good predictor of fat.
The thing that's interesting or annoying from our aggression point of view is that,
triceps and thigh are themselves very strongly correlated.
And so, that can produce problems in a regression model.
Your coefficients maybe hard to estimate,
interval estimates may become wider rather elusive to statistical power.
There are reasons why we would like to not have too many correlated variables in
one regression model.
So, we're not really going to explore a multicollinearity in any kind of
systematic way.
What we are going to do is, confirm numerically that,
yes, the correlations between fat and triceps and thigh and fat,
they're both pretty high and also between thigh and triceps.
Our job right now is to try to measure the correlation of thighs and triceps for
instance.
After we control for, we'll hear people say, partialing out and
after we partial out on thigh, what we're going to do is look at residuals.
So if that seems unmotivated, think about it like this, we'll try to take fat and
predict it using thigh.
So we're trying to find the linear component of thigh in fat,
speaking loosely.
If we look at the residuals,
we're extracting the linear predictive power of thigh on fat.
We'll do the same thing with thigh and triceps.
We're essentially subtracting at the linear relationship here.
After that's removed, we then see how fat and triceps are correlated.
And we'll call that a partial correlation of fat and triceps.
To operationalize, this is really quite simple.
Lm is the command that'll give us the linear model.
And if you're comfortable with R,
you're probably comfortable nesting commands like this.
We'll do a linear regression of fat and thigh.
We'll interrogate that model with the predict command
in order to give us our hat values.
Things with a hat on them are things being estimated, so
fat.hat is how fat is estimated in the model used in thigh, linear model.
Same thing, corresponding thing with triceps.hat.
Once we're done with that, we'll subtract off,
we'll essentially look at the residuals, and we see that the partial correlation
of fat in triceps after thigh's been partialled out is around 17%.
If you're lazy or I'd like to say efficient,
then there's a library that will do this for you.
It's a very popular thing to do.
If you run ppcor as a library, then there's a command there called pcor.
Again, put your variables in a matrix and run pcor and
you'll get the customary table.
And you can see that, that's many significant digits.
We've calculated the exact same quantity.
Now from a time series point of view, when you have an ARp model,
you'd probably like to partial out more variables than just one.
In this example, we stay with our body fat model and
show how to partial out a couple variables.
It's really the same process.
Build a model of fat on thigh and mid-arm, for instance.
So we're going to partial out thigh and mid arm.
Build a model predicting fat off of thigh and mid-arm, and then,
subtract the linear component.
Do the same thing with triceps.
We're taking the linear predictor of fat on thigh and
mid-arm, and essentially, getting rid of that linear contribution.
We'll take a correlation.
Does it surprise you that the partial correlation, in this case, is higher?
Now, how does this work when we're dealing with the time series?
We have, let's say stochastic variables apart from X theta, Xt+h.
We'll try to find the effect of X of t on X of t + h, all the way to the right,
after we control for or partial out the intervening random variables.
So, we're going to use I think a very natural notation,
x hat t + h will be the value predicted at the position,
t + h, by using the several random variables preceding.
We won't include Xt in the model, but we'll go from Xt + 1,
all the way through X sub t + h- 1.
In other words, the variables in the middle.
The subscripts that we're using on our coefficients for the betas
are really just telling you how far away you are from the thing you're predicting.
So beta1 is just one step away from x hat t + h,
beta2 is the coefficient, two steps away and so on.
Interestingly, due to stationarity,
we can find a relationship for X hat t using the very same variables.
We're going to use the same coefficients, but
look which coefficients are with which variables now?
Again, the subscript from the data tells you how far away you
are from the thing you're trying to predict.
So, beta1 is one way, beta2 is two way and so on.
Now we're going to suppress some details on just how this is done
in a time series rather than, as you see here for stochastic process.
So we won't worry about how the estimation is done.
But at this point, I think you can see what we're about to do.
We've got a predictor for X hat t + h.
We got a predictor for X hat t.
And what we'll do is partial out the intervening the random variables in
the middle.
We'll do that by looking at the residuals, and then finding a correlation.
So we're going to remove the linear effects of all terms in between.
That's how your partial autocorrelation plot is obtained.
By getting rid of the linear effects of terms between two random variables at
a certain lag, or a certain distance away.
At this point, especially in a simple linear regression context, you should feel
very comfortable partialling out a variable and you should know now and
be able to a friend, just what it is the PACF is measuring.
Explora nuestro catálogo
Inscríbete de manera gratuita y obtén recomendaciones personalizadas, actualizaciones y ofertas.
Coursera brinda acceso universal a la mejor educación del mundo, al asociarse con las mejores universidades y organizaciones, para ofrecer cursos en línea.
© 2018 Coursera Inc. Todos los derechos reservados.
