Time series forecasting with LSTMs directly has shown little success. This is surprising as neural networks are known to be able to learn complex non-linear relationships and the LSTM is perhaps the most successful type of recurrent neural network that is capable of directly supporting multivariate sequence prediction problems. A recent study performed at Uber AI Labs demonstrates how both the automatic feature learning capabilities of LSTMs and their ability to handle input sequences can be harnessed in an end-to-end model that can be used for drive demand forecasting for rare events like public holidays. In this post, you will discover an approach to developing a scalable end-to-end LSTM model for time series forecasting. In this post, we will review the 2017 paper titled "Time-series Extreme Event Forecasting with Neural Networks at Uber" by Nikolay Laptev, et al. presented at the Time Series Workshop, ICML 2017.