MinneBOS

Time series data is ubiquitous: weekly initial unemployment claim, daily term structure of interest rates, tick level stock prices, weekly company sales, daily foot traffic recorded by mobile devices, and numerous google search terms, just to name a few.

Some of the most important and commonly used data science techniques in time series forecasting are those developed in the field of machine learning and statistics. Data scientists and quants should have at least a few basic time series statistical and machine learning modeling techniques in their toolkit.

Given the resurgence of neural network-based techniques in recent years, it is important for data science practitioner to understand how to apply these techniques and the tradeoffs between neural network-based and traditional statistical methods. This lecture discusses two specific techniques:
Vector Autoregressive (VAR) Models and Recurrent Neural Network (RNN). The former is one of the most important class of multivariate time series statistical models applied in finance while the latter is a neural network architecture that is suitable for time series forecasting. I will demonstrate how they are implemented in practice and compares their advantages and disadvantages. Real-world applications are used to illustrate these techniques.