  Short-term forecasting is an important tool in understanding environmental
processes. In this paper, we incorporate machine learning algorithms into a
conditional distribution estimator for the purposes of forecasting tropical
cyclone intensity. Many machine learning techniques give a single-point
prediction of the conditional distribution of the target variable, which does
not give a full accounting of the prediction variability. Conditional
distribution estimation can provide extra insight on predicted response
behavior, which could influence decision-making and policy. We propose a
technique that simultaneously estimates the entire conditional distribution and
flexibly allows for machine learning techniques to be incorporated. A smooth
model is fit over both the target variable and covariates, and a logistic
transformation is applied on the model output layer to produce an expression of
the conditional density function. We provide two examples of machine learning
models that can be used, polynomial regression and deep learning models. To
achieve computational efficiency we propose a case-control sampling
approximation to the conditional distribution. A simulation study for four
different data distributions highlights the effectiveness of our method
compared to other machine learning-based conditional distribution estimation
techniques. We then demonstrate the utility of our approach for forecasting
purposes using tropical cyclone data from the Atlantic Seaboard. This paper
gives a proof of concept for the promise of our method, further computational
developments can fully unlock its insights in more complex forecasting and
other applications.
