# Simple Flux LSTM for Time Series

**URL:** <https://discourse.julialang.org/t/simple-flux-lstm-for-time-series/35494>\
**Category:** Machine Learning\
**Tags:** question, flux, time-series, machine-learning\
**Created:** [March 4, 2020, 1:10am UTC](https://discourse.julialang.org/t/simple-flux-lstm-for-time-series/35494 "2020-03-04T01:10:12Z")\
**Posts on this page:** 1\
**Showing post:** 37

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**Author:** ![ToucheSir](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/touchesir/32/14411_2.png) [@ToucheSir](https://discourse.julialang.org/u/ToucheSir)\
**Post date:** [April 7, 2022, 1:43am UTC](https://discourse.julialang.org/t/simple-flux-lstm-for-time-series/35494/37 "2022-04-07T01:43:40Z")

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There are at least a few discussions and examples/MWEs around this kind of univariate time series forecasting with Flux RNNs floating around community forums. [How to train Flux to learn a sequence conditional to some initial "seeds"?](https://discourse.julialang.org/t/how-to-train-flux-to-learn-a-sequence-conditional-to-some-initial-seeds/78330) is a recent example I just remembered. The reason such a thing does not exist in the model zoo is probably two-fold:

1. Model zoo entries don’t write themselves 😛
2. A LSTM is a big hammer to model a 30-sample univariate timeseries forecasting problem with. Generally we try to strike a balance between clear, brief files and sufficiently “common” or “interesting” datasets and tasks in the model zoo to differentiate it from tutorials. In this case, perhaps something like forecasting with a UCI benchmark dataset would be appropriate.

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