How to make forecasts strictly positive?

• scikit-hts version: 0.5.5
• Python version: 3.6
• Operating System: Windows

Description

Want to make my predictions to be strictly positive. For that, I want to make use of the log transformation function and I’ve passed a custom transformation function to the transform parameter. The final results still have negative forecasts. I want to know if I’m passing the custom function correctly. If yes, want to know why the results are negative?.

I’ve created the custom function in 2 ways. Here goes the first one.

from collections import namedtuple
transform = namedtuple('transform', {'func': 'Callable', 'inv_func': 'Callable'})
transformer = transform(np.log1p, np.exp)
htsmodel = hts.HTSRegressor(model = 'auto_arima', revision_method = 'BU', n_jobs = 0, transform = transformer)

And the second one

transformer = hts._t.Transform(np.log1p, np.exp)
htsmodel = hts.HTSRegressor(model = 'auto_arima', revision_method = 'BU', n_jobs = 0, transform = transformer)

Unfortunately, None of them have any effect in bringing out the positive forecasts. So, Am I passing them the wrong way? or It’s something else.

PS: When I’m transforming them explicitly before fitting and back-transforming them after predictions. The forecasts are positive.

[carlomazzaferro]

@aakashparsi good catch, not sure how this passed the tests but the culprit is this:

>>> from collections import namedtuple
>>> Employee = namedtuple('Employee', ['name', 'id'])
>>> employee = Employee('Guido', 1)
>>> employee
Employee(name='Guido', id=1)
>>> from typing import NamedTuple
>>> isinstance(employee, NamedTuple)
False  # I assumed this was True

Need to make an adjustment to the code, will lyk when it is in.

[carlomazzaferro]

0.5.10 on the release pipeline: https://github.com/carlomazzaferro/scikit-hts/actions/runs/909157630