4

I'm modeling some time series data ($\{y_t\}_t$) and would like to construct a model that is able to return not just a single-value prediction $\hat{y_t}$, but an interval $C_t=(\hat{y}_{t, lower}, \hat{y}_{t, upper})$ such that $y_t \in C_t$ with some probability.

Now, I've learned about the pinball loss:
$$L(q, z)=\cases{qz, & z >= 0 \\ (q-1)z,& z<0}$$
If I understand it correct, $q \in (0,1)$ is the quantile that I want to predict, and $z$ is the difference between the actual value $y$ and what my model predicted $\hat{y}$. If the model is trained to optimize this loss for some $q$, it will return me the estimate $\hat{y}^{(q)}_t$ such that $P(y_t < \hat{y}^{(q)}_t)=q$. **Is this interpretation correct?**

Then, can I simply train two models, say one for $q=0.05$ and the other for $q=0.95$ in order to get the estimates of the intervals that contain the actual values with the probability $0.95-0.05=0.9$?

Thanks for the answer. Did you mean to write $y_t$ instead of $C_t$ in the equation at the end of your answer? – Milos – 2018-06-23T11:19:58.480

Apologies. It is $y_t$. I will correct it. – Kiritee Gak – 2018-06-23T12:12:44.383