Jeffrey is the Chief Data Scientist at AllianceBernstein, a global investment firm managing over $550 billions. In this role, he is responsible for leading the global data science group, partnering with investment professionals to create investment signals, and collaborating with sales and marketing teams to optimize sales. Graduated with a Ph.D. in economics from the University of Pennsylvania, he has also taught statistics, econometrics, and machine learning courses at UC Berkeley, Cornell, NYU, the University of Pennsylvania, and Virginia Tech. Previously, Jeffrey held data science and analytic leadership positions at Silicon Valley Data Science, Charles Schwab Corporation, and KPMG.
Time Series Forecasting Using Recurrent Neural Network and Vector Autoregressive Model: When and How
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 trade-offs between neural network-based and traditional statistical methods. This lecture discusses two specific techniques, real-world applications and their advantages and disadvantages, and demonstrations of exploratory time series data analysis.
- Vector Autoregressive (VAR) Models – one of the most important class of multivariate time series statistical models applied in finance.
- Recurrent Neural Network (RNN) - a neural network architecture suitable for time series forecasting.