<p>Yes, you can do Multi-Horizon forecasting with LSTM. You can find a similar architecture by Amazon <a href="https://arxiv.org/pdf/1711.11053.pdf" rel="nofollow noreferrer">here</a>. </p>

<p>PS: these type of questions seems more suitable for <a href="https://datascience.stackexchange.com">https://datascience.stackexchange.com</a>. </p>
Can LSTM solve the particular problem I currently have
<p>As far as I know, LSTM can do regression/classification analysis in terms of time series data.  I am wondering if </p>

<pre><code>time  feature1 feature2 feature3 output
day1 ....................................
day2.....................................
day3....................................
day4...................................
day5...................................
...
...
day30.................................
</code></pre>

<p>Assume I got the data from experiment and totally collected 30 days' data with format shown above. Using LSTM I definitely can predict the output of 31 day as long as I obtain the input (feature 1 to 3 in this case) in day 31.  My question is if I miss the experimental input data from day 31 to 50 (too busy to do experiment), could I still use LSTM to predict the output of 51 days? (I may have time to do the experiment again in day 51 ^.^).  </p>

<p>This problem essentially is unlike the stock prediction problem that can be typically analyzed by LSTM. Since in the stock prediction problem, the output in time t can be considered as the input in time t+1. However, in this particular problem, the input (feature 1 to 3) can not be directly linked to the output.</p>

<p>Anyone can help to clarify/solve?</p>

<p>Thank you very much.</p>