Looking at the prophet example (see here ), I notice that at the end when he makes an out-of-sample prediction, he does it by manually drawing his own samples and using the inferred quantities from the model to model the trend in the future. My question is this: Is there a way to do OOS time-series …

Does this help? If your observed variable is a pandas series or data frame column with missing values, pymc3 will automatically ‘interpolate’, which in this case is actually ‘predicting’: https://gist.github.com/DanielWeitzenfeld/bbfe8246dc20df77eee8e48ac12d65de extrapolating random walk.i…

Yes! this is great! Are there any docs anywhere I can read about this automatic interpolation?

@DanWeitzenfeld thanks for the notebook and the suggestion. I’ve tried to implement it with a simple linear regression model using GaussianRandomWalk, and it fails with the below error. `Auto-assigning NUTS sampler... Initializing NUTS using jitter+adapt_diag... Multiprocess sampling (4 chains in 4…

The trick, AFAICT, is to pass either a pandas series or a pandas data frame column as observed.

@DanWeitzenfeld , thanks! this seems to do it, but this seems odd given the preference for using theano.shared tensors or numpy.arrays in most cases.

is there any tricks for doing a similar thing with Pymc (v5)? I get the following error when I try the above in Pymc v5: NotImplementedError: Automatic inputation is only supported for univariate RandomVariables. obs of type <class ‘pymc.distributions.timeseries.RandomWalkRV’> is not supported. t…

Out-of-sample random-walk time series prediction

Questions

DanWeitzenfeld May 5, 2023, 9:33pm 9

see here

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Out-of-sample random-walk time series prediction

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