论文标题
对时间序列预测的基于回归的损失功能的全面调查
A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting
论文作者
论文摘要
时间序列预测一直是研究的积极领域，因为它的许多应用程序包括网络使用预测，资源分配，异常检测和预测性维护。在过去五年中发表的大量出版物提出了各种客观损失功能集，以解决诸如偏见数据，长期预测，多共线功能等案例。在本文中，我们总结了14个众所周知的回归损失函数，用于时间序列预测，并列出了其应用程序可以帮助更快地进行更快和更好的模型转化的情况。我们还展示了某些类别的损失功能在所有数据集中的表现如何，并且在数据分布未知的情况下，可以被视为基线目标函数。我们的代码可在github：https：//github.com/aryan-jadon/regress-loss-functions-intime-series-forecasting-tensorflow上找到。
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.
