# Forecasting using Gaussian Random Walk

**URL:** <https://discourse.pymc.io/t/forecasting-using-gaussian-random-walk/10565>\
**Category:** v5\
**Tags:** modeling\
**Created:** [October 8, 2022, 8:14pm UTC](https://discourse.pymc.io/t/forecasting-using-gaussian-random-walk/10565 "2022-10-08T20:14:43Z")\
**Posts on this page:** 1\
**Showing post:** 2

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**Author:** ![jessegrabowski](https://yyz2.discourse-cdn.com/flex036/user_avatar/discourse.pymc.io/jessegrabowski/32/5010_2.png) [@jessegrabowski](https://discourse.pymc.io/u/jessegrabowski)\
**Post date:** [October 8, 2022, 9:51pm UTC](https://discourse.pymc.io/t/forecasting-using-gaussian-random-walk/10565/2 "2022-10-08T21:51:43Z")

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I wrote a post about the statistics of the GRW model and how it should forecast [here](https://discourse.pymc.io/t/sample-posterior-predicitve-not-catching-shape-of-new-data/10179/10). Applying it in your case would be a bit more complex. You would use the procedure I outline in that post to construct that latent GRW process for t+h forecast steps of `raw_rw`, then combine it with all the other time-invariant parts of your model posterior to generate forecasts.

Don’t expect too much from the GRW out of sample.

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