Objectives

• Introduction to the open-source Darts library for time series forecasting in Python
• Get hands-on experience in using traditional statistical-based methods (Exponential
  Smoothing, ARIMA), machine learning models (Linear Regression, LightGBM) and
  deep-learning models (N-BEATS) for time series forecasting
• Evaluate and compare the performance of the different methods as well as interpret the
  prediction of these models
• Leverage meta-learning methods to forecast previously unseen time series
• Practice with well-known datasets M4 and M3 which contains a wide variety of time
  series