A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting
It provides a practical guide for researchers and practitioners in time series forecasting, but it is incremental as it summarizes existing methods.
This paper surveys 14 regression loss functions for time series forecasting, identifying which ones aid in faster and better model convergence and perform well across datasets as baselines when data distribution is unknown.
Time Series Forecasting has been an active area of research due to its many applications ranging from network usage prediction, resource allocation, anomaly detection, and predictive maintenance. Numerous publications published in the last five years have proposed diverse sets of objective loss functions to address cases such as biased data, long-term forecasting, multicollinear features, etc. In this paper, we have summarized 14 well-known regression loss functions commonly used for time series forecasting and listed out the circumstances where their application can aid in faster and better model convergence. We have also demonstrated how certain categories of loss functions perform well across all data sets and can be considered as a baseline objective function in circumstances where the distribution of the data is unknown. Our code is available at GitHub: https://github.com/aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-Tensorflow.