Speaker
Oleksandr Honchar
[HPA – University of Verona]
Program, Abstract and timetable
Lecture 1: Introduction to machine learning and time series analysis
Lecture 2: Data preparation and feed-forward neural networks
Lecture 3: Convolutional and recurrent neural networks
Lecture 4: Building a trading strategy and further applications
Abstract
In this mini-course we will study artificial neural networks as a tool
for time series classification and forecasting. We will review
theoretical concepts of feed-forward, convolutional and recurrent neural
networks, their modern architectures and implement them in Python. The
emphasis of the mini-course is on practical applications, so we will use
before mentioned algorithms to forecast stock prices movements and
build a real asset trading strategy and backtest it. After attending
this course, students will understand basic pipeline of machine learning
based time series analysis: data preprocessing, fitting the model,
evaluation of results and will be able to use their own models for
building algorithmic trading strategies.