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Hi @rurjais - I'm curious... did you learn the answer to your question(s)? |
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I am trying to understand DeepAR in a very precise way, so I have a question regarding what does actually mean the distr_output attribute in DeepAR estimator:
y-\hat{y}
that we want our residuals to follow?I am guessing it is the first one, but if that is the case, then I have a doubt I would like someone to help me out with. I'll try to be clear :)
I also have a question about what is the avg_epoch_loss that shows during training. What loss is this...? I give the code below for further help :)
I created a simple linear array, onto which I add some Random Poisson noise:
Then, as I wanted to perform univariate analysis (although I understand that this is not the most proper task for DeepAR to be trained on) I create the train and test dataset:
The I created my estimator:
After that I built my predictions:
Then worked around the results to handle them better:
A plot for predictions is annexed (output.png file).
![output](https://user-images.githubusercontent.com/76950496/210858309-e39a2580-9679-43ec-836c-13e08ef0014d.png)
At this point, I checked the predictions were not good, along with residuals:
residuals = test_set.values-forecast_entry.median.reshape(94,1) #the issue with reshape is for plotting
As one can see, residuals are something else but normal (see plot annexed) (output_2.png file)
![output_2](https://user-images.githubusercontent.com/76950496/210858350-e8a76fdd-4961-42bc-9882-ead2f6675628.png)
I performed a KS test on residuals (although I know it may not be proper for discrete data), but it just confirmed that residuals are not normal...
So now my questions are:
distr_output
actually modeling the target or the noise model? I assume it is the target, but why does my model behave so poorly if that is the case? Is it because the linear component is the one that dictates the most the behavior ofs_2
series and soPoissonOutput()
will not catch any predicted behavior?s
as a covariate could help in getting good predictions ofs_2
?) and is it only competitive when we use hundreds of time-series as covariates?Thank you all
Ricardo
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