Date & Time
September 9, 2022, 11:00 am – 12:00 pm
Title: Deep Learning Based Methods for Time Series Forecasting
Time series forecasting has long been a key and well-studied area of academic research and has many important applications in topics such as commercial decision making in retail, finance, product development and planning, biological sciences and medicine to name a few. In contrast to traditional methods for time series forecasting which focus on parametric models informed by domain expertise and rely heavily on well-designed features, modern machine learning methods, especially deep learning which gained popularity in recent times, try to learn temporal dynamics in a purely data-driven manner. With the increasing data availability and computing power in recent times as well as their success in practical applications and competitions, deep learning-based forecasting models have become a popular choice for many time series forecasting tasks. Given the diversity of time-series applications across various domains, many neural networks based models specifically designed for the forecasting tasks have been proposed. In this talk, we give a selective introduction and overview of the deep learning based time series forecast methods. We will present important building blocks for deep forecasting in some depth and focus on a so-called transformer block, a recent neural network architecture based on the attention mechanism which has received increased interest in applying for long sequence time series forecasting tasks.