论文标题
for2for：学习从预测中进行预测
For2For: Learning to forecast from forecasts
论文作者
论文摘要
本文提出了一个时间序列预测框架，该框架结合了标准预测方法和机器学习模型。机器学习模型的输入不是滞后值或常规时间序列特征，而是标准方法产生的预测。机器学习模型可以是卷积神经网络模型或复发性神经网络模型。这种方法背后的直觉是，时间序列的预测本身就是表征该系列的良好特征，尤其是当建模目的是预测时。也可以将其视为加权合奏方法。在M4竞赛数据集上进行了测试，此方法的表现优于季度系列的所有提交，并且比每月系列的获胜算法都更准确。
This paper presents a time series forecasting framework which combines standard forecasting methods and a machine learning model. The inputs to the machine learning model are not lagged values or regular time series features, but instead forecasts produced by standard methods. The machine learning model can be either a convolutional neural network model or a recurrent neural network model. The intuition behind this approach is that forecasts of a time series are themselves good features characterizing the series, especially when the modelling purpose is forecasting. It can also be viewed as a weighted ensemble method. Tested on the M4 competition dataset, this approach outperforms all submissions for quarterly series, and is more accurate than all but the winning algorithm for monthly series.
