# Need help with multivariate multi-step timeseries prediction using RNN

**URL:** https://discourse.julialang.org/t/need-help-with-multivariate-multi-step-timeseries-prediction-using-rnn/54966
**Category:** Machine Learning
**Created:** [February 10, 2021, 5:29am UTC](https://discourse.julialang.org/t/need-help-with-multivariate-multi-step-timeseries-prediction-using-rnn/54966 "2021-02-10T05:29:40Z")
**Posts on this page:** 1
**Page:** 1

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### Author: ![UtkarshChemE](https://sea2.discourse-cdn.com/julialang/user_avatar/discourse.julialang.org/utkarshcheme/32/17358_2.png) [@UtkarshChemE](https://discourse.julialang.org/u/UtkarshChemE)
#### Post date: [February 10, 2021, 5:29am UTC](https://discourse.julialang.org/t/need-help-with-multivariate-multi-step-timeseries-prediction-using-rnn/54966/1 "2021-02-10T05:29:40Z")

</div>

My network is as follows:

1. LSTM Encoder (Take a sequence of `n` lags and output a hidden `end` vector)
2. Fully Connected Dense Layer (To create a context vector)
3. Decoder network (LSTM layer that process context vector for `m` lags)

I am having trouble with the decoder part. Basically, I need to `repeat` my context vector and then apply LSTM recursively. Any suggestion on how to do that would be helpful? For multi-step output is there a better method?