Trends in time series are valuable. If the cost of a product rises suddenly, it likely indicates a sudden shortfall in supply or a sudden rise in demand. If the cost of claims filed by a patient rises sharply, it plausibly suggests rapidly worsening health.

But how do we estimate the trend at a particular time in a noisy time series? The answer is simple: smooth the time series using any one of the many methods, local polynomials or via GAMs or similar such methods, and then estimate the derivative(s) of the function at the chosen point in time. Smoothing out the noise is essential. If you don’t smooth and instead go with a naive estimate of the derivative, it can be heavily negatively correlated with derivatives gotten from smoothed time series. For instance, in an example we present, the correlation is –.47.

#### Clarification

Sometimes we want to know what the “trend” was over a particular time window. But what that means is not 100% clear. For a synopsis of the issues, see here.

#### Python Package

incline provides a couple of ways of approximating the underlying function for the time series:

- fitting a local higher order polynomial via Savitzky-Golay over a window of choice
- fitting a smoothing spline

The package provides a way to estimate the first and second derivative at any given time using either of those methods. Beyond these smarter methods, the package also provides a way a naive estimator of slope—average change when you move one-step forward (step = observed time units) and one-step backward. Users can also calculate the average or maximum slope over a time window (over observed time steps).