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’ll demonstrate how they are implemented in practice and compares their advantages and disadvantages. Real-world applications, demonstrated using python and Spark, are used to illustrate these techniques. While not the focus in this lecture, exploratory time series data analysis using time-series plot, plots of autocorrelation (i.e. correlogram), plots of partial autocorrelation, plots of cross-correlations, histogram, and kernel density plot, will also be included in the demo.
The attendees will learn – the formulation of a time series forecasting problem statement in context of VAR and RNN – the application of Recurrent Neural Network-based techniques in time series forecasting – the application of Vector Autoregressive Models in multivariate time series forecasting – the pros and cons of using VAR and RNN-based techniques in the context of financial time series forecasting – When to use VAR and when to use RNN-based techniques
Session hashtag: #SAISDL4
Jeffrey is the Head of Data Science at the Store Technology Group of Walmart U.S. Technology. His prior roles include the Chief Data Scientist / Global Head of Data Science at AllianceBernstein, a global asset-management firm that managed over $500 billions in assets, Vice President of Data Science at Silicon Valley Data Science, Head of Risk Analytics and Quantitative Modeling at Charles Schwab Corporation, and Director of Risk Consulting at KPMG. He has also taught economics, econometrics, finance, statistics, and machine learning at UC Berkeley, Cornell, NYU, University of Pennsylvania, and Virginia Tech. Jeffrey is active in the data science community and often speaks at data science conferences in the U.S., Europe, and Asia. He has many years of experience in applying a wide range of econometric and machine learning techniques to create analytic solutions for financial institutions, various businesses, and policy institutions. Jeffrey holds a Ph.D. and an M.A. in Economics from the University of Pennsylvania and a B.S. in Mathematics and Economics from UCLA.