TITLE:
A ResNet Convolutional Neural Network for Time Series data using InceptionNet paradigms
ABSTRACT:
The recent progress in Convolutional Neural Network (CNN) designs has extended their effectiveness to time series data as well. Time series features, which can be represented as one-dimensional vectors, open the door for training deeper networks using ResNet-inspired structures. Inception modules provide the capability to the network to handle objects and patterns of different sizes and capture both local details and global context. We propose an Inception-Residual CNN model tailored for time series data which will be applied on real-life data in retail.
ABOUT THE PRESENTER:
After graduating from the Faculty of Computer and Information Science at the University of Ljubljana in 2015, Vanja Mileski started working at the Jožef Stefan Institute (JSI). He was a Master’s student at the International Postgraduate School Jožef Stefan and a student researcher at the JSI. After finishing his Master’s studies, he applied his knowledge of data mining in the private sector as a Data Scientist in the retail, telecommunications, banking, stock market and insurance sectors.
His current research interests include time-series classification, deep learning, ResNet and Inception architectures.