排序方式: 共有1条查询结果,搜索用时 0 毫秒
1
1.
Artemios-Anargyros Semenoglou Evangelos Spiliotis Spyros Makridakis Vassilios Assimakopoulos 《International Journal of Forecasting》2021,37(3):1072-1084
The M4 competition identified innovative forecasting methods, advancing the theory and practice of forecasting. One of the most promising innovations of M4 was the utilization of cross-learning approaches that allow models to learn from multiple series how to accurately predict individual ones. In this paper, we investigate the potential of cross-learning by developing various neural network models that adopt such an approach, and we compare their accuracy to that of traditional models that are trained in a series-by-series fashion. Our empirical evaluation, which is based on the M4 monthly data, confirms that cross-learning is a promising alternative to traditional forecasting, at least when appropriate strategies for extracting information from large, diverse time series data sets are considered. Ways of combining traditional with cross-learning methods are also examined in order to initiate further research in the field. 相似文献
1