Hi,
PyMC3 has been renamed to PyMC. If this affects you and you have questions let me know how I, or any of the PyMC devs can help.

I would like to help fix the unit tests if you don't have anything special in mind.

Hi, Matthijs!

You’ve done a good job with your timeseers package! Thanks!
I’m a data scientist in a big retail company and we are now working on a new product in which we want to forecast sales. It seems like timeseers could be a great decision because we have lots of SKUs and it is really difficult to builld a model for each of them individually.
In our model we would like to use some exogenous regressors. It seems like timeseers already has a “Regressor” module. However, it is not very clear if it works and how it works. Could you please give some comments about it or some toy example how to use it? Does it work only with categorical features or continuous features can also be used? What “ON” argument is used for?

Thanks in advance for your reply!

Kate

When using the LogisticGrowth model the _y_scale attribute of the base class is not found. This seem to be because the method that actually sets this attribute is never called.

PyMC4 has now been released https://www.pymc.io/blog/v4_announcement.html#v4_announcement with various new features including better GPU support. Are there any plans to port this repo to PyMC4? Mostly this would involve just changing the imports from import pymc3 as pm to import pymc as pm but maybe also changing the way plots are handled https://www.pymc-labs.io/blog-posts/the-quickest-migration-guide-ever-from-pymc3-to-pymc-v40/ .

I noticed this closed issue #40 but I'm not sure that you actually changed anything in response to that?

I have been working on this feature, and had chance to play with it on some highly seasonal series. If you think this would be a nice feature, I can wrap it up, open a PR and hopefully share some plots.

lemme know if you'd like me to fix one.

Sometimes there is an unknown special effect that results in very high values in the time series. (let's say a hurricane). I might be okay not getting accurate predictions for those unknown special days. But the neighboring points might also be affected due to the Fourier seasonality. I think it could be nice to provide a model that is robust to outliers. One approach could be adding an option to use pm.StudentT for the observations.

PyMC3 docs has a nice example that includes other (more advanced) methods as well. GLM: Robust Regression example

Here is a motivating example:

from timeseers.utils import seasonal_data
from timeseers import FourierSeasonality, LinearTrend
import numpy as np
import pandas as pd

np.random.seed(42)

df, _ = seasonal_data(5)

df.loc[630, "value"] = 100.
df.loc[631, "value"] = 100.
df.loc[632, "value"] = 100.
df.loc[633, "value"] = 100.

model = FourierSeasonality(n=5, period=pd.Timedelta(days=365)) + LinearTrend(n_changepoints=1)
model.fit(df[["t"]], df["value"], tune=2000, cores=1, chains=1)
model.plot_components(X_true=df, y_true=df['value'])

Using normal distribution I get the following predictions.

Changing to StudentT (two line diff), I get:

Do you think this would be a useful addition to the library?

Afterthought:
There is a similar discussion on fbprophet too. See: #1500.

It might be nice if the users could define the likelihood in TimeSeriesModel as they please.

For example, line 38 - 44 in linear_trend with n_groups=1 makes delta a matrix of 1 by n_changepoints. It might be faster if this is just a vector in that case?

and the tagline should be time should be taken seer-iously

Hi there,

Would it be possible to add timeseers to Pypi so that one could use pip install timeseers to install it?

Thanks

Hey,

I was browsing through the codebase and found it very interesting, considering playing a bit more with it.

I saw then, that in some modules, there seems to be an unnecessary dependency on the theano.

No big issue, but more accessible if this would be removed while being quite a quick fix. What do you think?

Please consider adding a license to make clear how this code can be used
https://choosealicense.com/no-permission/

# 1) class option
model = LinearTrend() + FourierSeasonality()
model = HierarchicalLinearTrend(group='subject') + FourierSeasonality()
model = GroupedLinearTrend(group='subject') + FourierSeasonality()

# 2) metaclass option
model = LinearTrend() + FourierSeasonality()
model = Hierarchical(LinearTrend(), on='subject') + FourierSeasonality()
model = Distinct(LinearTrend(), on='subject') + FourierSeasonality()

# 3) seperate hierarchy object 1
partial_subject = Share(type='partially_shared', on='subject')
distinct_subject = Share(type='distinct', on='subject')

model = LinearTrend() + FourierSeasonality()
model = LinearTrend().share(partial_subject) + FourierSeasonality()
model = LinearTrend().share(distinct_subject) + FourierSeasonality()

# 4) seperate hierarchy object 2
shared_subject = Share(type='shared')  # default
partial_subject = Share(type='partially_shared', on='subject')
distinct_subject = Share(type='distinct', on='subject')

model = LinearTrend(share=shared_subject) + FourierSeasonality(share=shared_subject)
model = LinearTrend(share=partial_subject) + FourierSeasonality()
model = LinearTrend(share=distinct_subject) + FourierSeasonality()

# 5) keyword option
model = LinearTrend(shared='full') + FourierSeasonality()  #  shared='full' is default
model = LinearTrend(group='subject', shared='partial') + FourierSeasonality()
model = LinearTrend(group='subject', shared='none') + FourierSeasonality()

# Cannibalism in this option
model = Constant(range=(0, None), shared='none') + LinearTrend(shared='full') + ((FourierSeasonality(perios=365) + FourierSeasonality(period=7)) * Constant(range=[-1, 1], group='subject', shared='none'))
model = LinearTrend() + (FourierSeasonality(perios=365) * Constant(range=[-1, 1], group='subject', shared='none')) + FourierSeasonality(period=7)

# 6) keyword option
model = LinearTrend() + FourierSeasonality()
model = LinearTrend(partially_shared_on='subject') + FourierSeasonality()
model = LinearTrend(distinct_on='subject') + FourierSeasonality()

# 7) bitwise or Operator option
model = LinearTrend()                                         # single estimation for all subjects
model = (LinearTrend() | 'subject') + FourierSeasonality()    # hierarchical estimation
model = (LinearTrend() || ('subject') + FourierSeasonality()  # independent estimation

#return day well spent

We noticed that if we run the a model with a LinearTrend without any arguments it errors with an unclear error message: " unsupported operand type(s) for +: 'NoneType' and 'int' "

Caused by shape of expected beta and model beta

Hi Matthijs ! Just watched your PyData talk from Oct 2020 and would love to try using this model package in a production codebase. It appears the main missing element is a robust prediction api. Is this still the case?

If so, would you be willing to invest some time either adding this functionality or guiding me to do it? I would also be happy to pitch in some level of on-going maintenance if so.

Something like this?

LinearTrend() + (Constant(hierarchical_on='subgroup_col) * FourierSeasonality())

Sorry for writing this question as an issue. One of the applications I deal with is forecasting short-mid life sku (2-10months). With prophet, you cannot model the trend for the product lifecycle with introduction-growth-maturity-decline phases. However, we can model trend with some kind of scikit/random forest type model based on other SKUs that have completed their lifecycle.

Is there any way to insert an external model for the trend component, for an application such as above?