<p>I agree with dyukha that it depends. LSTM have shown good performance on forecasting on different tasks: <a href="https://eng.uber.com/m4-forecasting-competition/" rel="nofollow noreferrer">this approach</a> with LSTM won the M4 competition, Amazon uses LSTM for forecasting as well <a href="https://arxiv.org/abs/1704.04110" rel="nofollow noreferrer">paper</a>, <a href="https://medium.com/@julsimon/predicting-world-temperature-with-time-series-and-deepar-on-amazon-sagemaker-e371cf94ddb5" rel="nofollow noreferrer">blog</a>.</p>

<p>Implementing such approaches from scratch is doable but can be a bit tricky if you are not familiar with neural network framework. However, you can use directly implementations from <a href="https://github.com/awslabs/gluon-ts" rel="nofollow noreferrer">Gluon-ts</a> which contains different type of neural network architecture (LSTM, convolution, feedforward, attention, etc) that you can try to see which one works best with your data.</p>
What kind of Neural Networks can I use for forecasting?
<p>I am thinking of using Neural Networks for forecasting. What kind of Neural Networks are suitable for this occasion? Are RNN suitable? LSTM? Thank you...</p>