trendfilter 0.1.4

Useful trendfilter algorithm

trendfilter:

Trend filtering is about building a model for a 1D time series that has some nice properties such as smoothness or sparse changes in slopes (piecewise linear).

The objective to be minimized is, in our case, Huber loss with regularization on 1st, 2nd or 3rd derivative plus some constraints. Can be either L1 or L2 norms for regularization.

This library provides a flexible and powerful python function to do this and is built on top of the cvxpy optimization library.

Install

pip install trendfilter

or clone the repo.

Examples:

Contruct some x, y data where there is noise as well as few outliers. See test file for prep_data code and plotting code.

First build the base model with no regularization. This is essentially just an overly complex way of constructing an interpolation function. If you use the keyword return_function, it returns an actual function which in interpolation of the model. Without that, it return an array, evaluated at the set of x points.

x, y_noisy = prep_data()
y_fit = trend_filter(x, y_noisy)

This has no real use by itself. It's just saying the model is the same as the data points.

You'll always want to give it some key-words to apply some kind of regularization or constraint so that the model is not simply the same as the noisy data.

Let's do something more useful. Don't do any regularization yet. Just force the model to be monotonically increasing. So basically, it is looking for the model which has minimal Huber loss but is constrained to be monotonically increasing.

We use Huber loss because it is robust to outliers while still looking like quadratic loss for small values.

y_fit = trend_filter(x, y_noisy, monotonic=True)

The green line, by the way, just shows that the function can be extrapolated which is a very useful thing, for example, if you want to make predictions about the future.

Ok, now let's do an L1 trend filter model. So we are going to penalize any non-zero second derivative with an L1 norm. As we probably know, L1 norms induce sparseness. So the second dervative at most of the points will be exactly zero but will probably be non-zero at a few of them. Thus, we expect piecewise linear trends that occasionally have sudden slope changes.

y_fit = trend_filter(x, y_noisy, l_norm=1, alpha_1=0.2)

Let's do the same thing but enforce it to be monotonic.

y_fit = trend_filter(x, y_noisy, l_norm=1, alpha_1=0.2, monotonic=True)

Now let's increase the regularization parameter to give a higher penalty to slope changes. It results in longer trends. Fewer slope changes. Overall, less complexity.

y_fit = trend_filter(x, y_noisy, l_norm=1, alpha_1=2.0)

Did you like the stair steps? Let's do that again. But now we will not force it to be monotonic. We are going to put an L1 norm on the first derivative. This produces a similar output but it could actually decrease if the data actually did so.

y_fit = trend_filter(x, y_noisy, l_norm=1, alpha_0=8.0)

Let's do L2 norms for regularization on the second derivative. L2 norms don't care very much about small values. They don't force them all the way to zero to create sparse solution. They care more about big values. This results is a smooth continuous curve. This is a nice way of doing robust smoothing.

y_fit = trend_filter(x, y_noisy, l_norm=2, alpha_1=1.0)

Let's use L1 norm again but put it on the 3rd derivative. This will result in sparse changes in second derivative. So locally things will be quadratic but with sparse changes.

We also have a key-word to constraint the function to pass through the origin, (0,0).

y_fit = trend_filter(x, y_noisy, l_norm=1, alpha_2=3.0, constrain_zero=True)

Here is the full function signature.

def trend_filter(x, y, y_err=None, alpha_2=0.0,
                 alpha_1=0.0, alpha_0=0.0, l_norm=2,
                 constrain_zero=False, monotonic=False,
                 return_function=False):

So you see there are alpha key-words for regularization parameters. The number n, tells you the n+1 derivative is being penalized. You can use any, all or none of them. The key-word l_norm gives you the choice of 1 or 2. Monotonic and constrain_zero, we've explained already.

The return_function allows you to get either the model at the points in an array (default) or the interpolating function of the model to apply to some other x values.

We didn't discuss y_err. That's the uncertainty on y. The default is 1. The Huber loss is actually applied to (data-model)/y_err. So, you can weight points differently if you have a good reason to, such as knowing the actual error values or knowing that some points are dubious or if some points are known exactly. That would be the limit as y_err goes towards zero and it will make the curve go through those points.

All in all, these keywords give you a huge amount of freedom in what you want your model to look like.

Enjoy!

First pypi version April 12, 2021

0.1.4 Added more examples with more documentation and plots in the Readme