系列(地层学)
人工神经网络
时间序列
计算机科学
回归
机器学习
人工智能
回归分析
计量经济学
数据挖掘
统计
数学
生物
古生物学
作者
Francisco Martínez,María Pilar Frías,Maria D. Pirez-Godoy,Antonio J. Rivera
出处
期刊:IEEE Access
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:10: 3275-3283
被引量:3
标识
DOI:10.1109/access.2022.3140377
摘要
Time series forecasting plays a key role in many fields such as business, energy or environment. Traditionally, statistical or machine learning models for time series forecasting are trained with the historical values of the series to be forecast. Unfortunately, some time series are too short to suitably train a model. Motivated by this fact, this paper explores the use of data available in a pool or collection of time series to train a model that predicts an individual series. Concretely, we train a generalized regression neural network with the examples drawn from the historical values of a pool of series and then use the model to forecast individual series. In this sense several approaches are proposed, including to draw the examples from a pool of series related to the series to be forecast or the training of several models with mutually exclusive series and the combination of their forecasts. Experimental results in terms of forecasting accuracy using generalized regression neural networks are promising. Furthermore, the proposed approaches allow to forecast series that are too short to build a traditional generalized regression neural network model.
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