Wei Cai
Data Scientist, Cox Communications

Wei Cai is a data scientist with Cox Communications, where she’s transitioned from building statistical models and doing analysis using R and SAS to developing machine learning models and processing streaming telemetry data using Python, Scala, and Java. She successfully developed both statistical models and deep learning models for the company to build a 10-year capacity planning budget, and she’s working on building models to compute to maximize network availability. She was born in China, earned her master’s degrees in actuarial science and business analysis from Georgia State University, and she’s seeking a master’s degree with a concentration in computing and analysis from the Georgia Institute of Technology. Her major areas of research interest are building deep learning models to help proactive network management.

Sessions

2:35pm–3:15pm Wednesday, September 11, 2019

Location: 230 C

Long-term real-time network traffic flow prediction using LSTM recurrent neural network

Models and Methods

Secondary topics: Deep Learning, Machine Learning, Temporal data and time-series

Wei Cai (Cox Communications)

Real-time traffic volume prediction is vital in proactive network management, and many forecasting models have been proposed to address this. However, most are unable to fully use the information in traffic data to generate efficient and accurate traffic predictions for a longer term. Wei Cai explores predicting multistep, real-time traffic volume using many-to-one LSTM and many-to-many LSTM. Read more.