A Crash Course in Sequential Data Prediction using RNN and LSTM

Learn data skills and how to apply them in the competitive finance industry to boost your portfolio with hands-on experience.

Statistics, machine learning, and deep learning skills have been growing in demand in the finance field over the past decade ⁠— ever since the global financial crisis of 2008. Firms actively seek employees with high mathematical and computing ability to model market risk, predict financial asset prices, and therefore mitigate their exposure to an economic downturn. Some well-known applications of data science in the finance industry include fraud detection, risk management, price prediction, and algorithmic trading.

Two tools that are imperative to these data science applications are recurrent neural networks (RNNs) and long short-term memory models (LSTMs), which are used to predict sequential data. The finance industry is going through a major revolution with the advent of deep learning and artificial intelligence ⁠— with competition getting tougher and companies scrambling to generate larger profits, the industry is shifting to using the latest technologies in AI to predict asset prices and manage the risk of their investment portfolios.

One way for you to try out RNNs and LSTMs and add them to your resume is by participating in bitgrit’s latest AI competition. bitgrit competitions are great to hone your skills in data prediction, and for this…