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Time Series Forecasting has been an active area of research due to its manyapplications ranging from network usage prediction, resource allocation,anomaly detection, and predictive maintenance. Numerous publications publishedin the last five years have proposed diverse sets of objective loss functionsto address cases such as biased data, long-term forecasting, multicollinearfeatures, etc. In this paper, we have summarized 14 well-known regression lossfunctions commonly used for time series forecasting and listed out thecircumstances where their application can aid in faster and better modelconvergence. We have also demonstrated how certain categories of loss functionsperform well across all data sets and can be considered as a baseline objectivefunction in circumstances where the distribution of the data is unknown. Ourcode is available at GitHub:https://github.com/aryan-jadon/Regression-Loss-Functions-in-Time-Series-Forecasting-Tensorflow.

A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting