Researchers design a user-friendly interface that helps nonexperts make forecasts using data collected over time. Whether someone is trying to predict tomorrow's weather, forecast future stock prices, identify missed opportunities for sales in retail, or estimate a patient's risk of developing a disease, they will likely need to interpret time-series data, which are a collection of observations recorded over time. Making predictions using time-series data typically requires several data-processing steps and the use of complex machine-learning algorithms, which have such a steep learning curve they aren't readily accessible to nonexperts. To make these powerful tools more user-friendly, MIT researchers developed a system that directly integrates prediction functionality on top of an existing time-series database. Their simplified interface, which they call tspDB (time series predict database), does all the complex modeling behind the scenes so a nonexpert can easily generate a prediction in only a few seconds. MIT researchers created a tool that enables people to make highly accurate predictions using multiple time-series data with just a few keystrokes.

Stock prediction is a topic undergoing intense study for many years. Finance experts and mathematicians have been working on a way to predict the future stock price so as to decide to buy the stock or sell it to make profit. Stock experts or economists, usually analyze on the previous stock values using technical indicators, sentiment analysis etc to predict the future stock price. In recent years, many researches have extensively used machine learning for predicting the stock behaviour. In this paper we propose data driven deep learning approach to predict the future stock value with the previous price with the feature extraction property of convolutional neural network and to use Neural Arithmetic Logic Units with it.

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F# is not a replacement, but a great complement for C#. Currently F# is used in many financial applications. Let's see how we can predict future stock prices with power of F#, C#, and Azure Machine Learning. In this session I will show you how to build F# Backend for estimating future stock prices with Azure Machine Learning, how to create Web API powered by F# with Suave Framework, and how to consume it from your ASP.NET Core App with Front-End powered by Aurelia and D3.js.