Time Series Prediction with TensorFlow

RNNs and LSTMs have enjoyed great success in text generation algorithms, but their use in other fields has not been as widely studied.  We will discuss our experiences and progress using Recurrent Neural Networks to make predictions on arbitrary multivariate time series data.  Our first study used weather data from the JFK terminal over several years using the TensorFlow framework.  We will discuss the issues related to tuning and validating this model, as well as how we migrated this model into the Model Asset Exchange, which is an IBM hosted API for making predictions on data using pre-trained neural network models. 

Our insight into tuning this model allowed us to provide another API via Watson Machine Learning, which is a hosted service that allows user defined data and models to be uploaded, trained, and tuned on GPU accelerated on demand hardware using simple remote API calls.  We will discuss examples from the financial sector, weather prediction, and other important time series prediction use cases.