The Critical Role of Loss Function Selection in Creating Accurate Time Series Forecasts
Mastering Time Series Forecasting with Machine Learning
Introduction
In this post I'll be demonstrating to you the importance of something that I believe is often overlooked in machine learning, the choice of loss function. I will do this by walking you through my approach to the Dengue Fever competition hosted by Driven Data.
I have built a Ridge regressor as my baseline model and several "flavours" of the XGBoost regressor each with a different loss function.
Competitors are asked to predict the total cases of dengue fever over weekly time intervals in Iquitos and San Juan. Each competitor is ranked according to the mean absolute error (MAE) their model(s) scores against the test data set. To learn more about the challenge, dengue fever or enter the competition yourself, you can visit the challenge homepage.
Notebooks & Repositories
I have made my working Jupyter notebook and GitHub repo available to you via the links below. The notebook may take a few minutes to load, please be patient.
