# SDE estimation based on a set of features
**URL:** https://discourse.julialang.org/t/sde-estimation-based-on-a-set-of-features/80798
**Category:** Machine Learning
**Created:** [May 10, 2022, 10:04am UTC](https://discourse.julialang.org/t/sde-estimation-based-on-a-set-of-features/80798 "2022-05-10T10:04:58Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@mobar](https://discourse.julialang.org/u/mobar)
#### Post date: [May 10, 2022, 10:04am UTC](https://discourse.julialang.org/t/sde-estimation-based-on-a-set-of-features/80798/1 "2022-05-10T10:04:58Z")
I would like to get started with non-linear time timeseries learning and extrapolation in Julia by learning the drift parameters in an SDE based on a pre-defined set of latent features (for this particular application it is not so important to estimate the noise term).
So given an SDE for the process X of the form:
dX(t) = f(V,t)X(t)dt+σX(t)dW(t)
where σ is a given constant, V is a vector of latent features and we can only make fairly minimum statements about how changes in a given feature should affect the drift - …what would then be a good way to approach the problem in Julia using SciML?
Thanks
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### Author:  [@ChrisRackauckas](https://discourse.julialang.org/u/ChrisRackauckas)
#### Post date: [May 10, 2022, 12:20pm UTC](https://discourse.julialang.org/t/sde-estimation-based-on-a-set-of-features/80798/2 "2022-05-10T12:20:47Z")
> [@mobar](#):
>
> where σ is a given constant, V is a vector of latent features and we can only make fairly minimum statements about how changes in a given feature should affect the drift - …what would then be a good way to approach the problem in Julia using SciML?
Here’s some examples:
[https://diffeqflux.sciml.ai/dev/examples/optimization\_sde/](https://diffeqflux.sciml.ai/dev/examples/optimization_sde/)
[https://turing.ml/dev/tutorials/10-bayesian-differential-equations/](https://turing.ml/dev/tutorials/10-bayesian-differential-equations/)