Q:

Forecast with predictable market events

I'm trying to build a predictive model to forecast the residual value of used electronic equipment. As a first step, I created a few quick plots in order to visually identify any interesting features. Here's an example:

A couple of things stood out:

the prices are 'steppy', i.e. they have a tendency to hug round
numbers and drop in chunks.
the price drops suddenly when a new
product is released.

The product release dates have a somewhat predictable cadence.
At first glance, it seems like this can be broken down into three parts:

price erosion before the new product launch
the impact of the new product launch
price erosion after the new
product launch

The pieces could then be stitched together.
Does this seem like a sensible way forecast, taking predictable market events into account? Are there any particular statistical methods/approaches you'd recommend for this sort of problem?

A:

Since you're interested in prediction, here are my thoughts.
Random Forests are excellent at regression tasks with abrupt discontinuities. 
See the figure on page 55 (60 of the PDF) of Decision Forests for Classification, Regression, Density Estimation, Manifold Learning and Semi-Supervised Learning.
Stochastic Gradient Boosting is a similar model worth considering. It's more sensitive to the hyperparameters, but it tends to be a top performer in regression tasks. GBM in R and Scikit-Learn in Python provide implementations of Stochastic Gradient Boosting that can be trained on quantile loss functions, giving you confidence intervals.
Following the advice given in this lecture, if you're forecasting beyond the immediate horizon then it is advisable to use multi-output models. Random Forests can do this well.
The idea behind nonparametric time-series prediction is that you create a function that approximates $$f(x_{i-1}, x_{i-2}, .., x_{i-n}) = x_i$$
The choice of $X$ is dependent on the data at hand. If your data is strictly positive, then consider a log-transform. If your data changes in scale as a function of time, then consider differencing. If there's an obvious cyclical trend to your data, then consider building a simple regression model over the period of the trend.
In any case, the final model for a predictive pipeline is feeding the residuals of these functions onto subsequent functions.
If you have side-information on market events, then it is advisable to include this into your model as an explanatory variable. A simple trick is include the event as a dummy variable.
$$
   k_i = \left\{
     \begin{array}{lr}
       \exp(\alpha(v - i)) & : i \ge v\\
       0 & : i < v
     \end{array}
   \right.
$$
Where $\alpha$ is a scaling factor and $v$ is the time index of the event that occurred. This looks like the following function.
As with all forms of predictive modelling, cross-validation is your friend. In the time-series case, your training, validation and testing set should be sequential.

