计算机科学
人工智能
领域(数学)
机器学习
系列(地层学)
统计模型
时间序列
技术预测
期限(时间)
深度学习
数学
古生物学
物理
量子力学
纯数学
生物
作者
Spyros Makridakis,Evangelos Spiliotis,Vassilios Assimakopoulos,Artemios-Anargyros Semenoglou,Gary Mulder,Κωνσταντίνος Νικολόπουλος
标识
DOI:10.1080/01605682.2022.2118629
摘要
The purpose of this paper is to test empirically the value currently added by Deep Learning (DL) approaches in time series forecasting by comparing the accuracy of some state-of-the-art DL methods with that of popular Machine Learning (ML) and statistical ones. The paper consists of three main parts. The first part summarizes the results of a past study that compared statistical with ML methods using a subset of the M3 data, extending however its results to include DL models, developed using the GluonTS toolkit. The second part widens the study by considering all M3 series and comparing the results obtained with that of other studies that have used the same data for evaluating new forecasting methods. We find that combinations of DL models perform better than most standard models, both statistical and ML, especially for the case of monthly series and long-term forecasts. However, these improvements come at the cost of significantly increased computational time. Finally, the third part describes the advantages and drawbacks of DL methods, discussing the implications of our findings to the practice of forecasting. We conclude the paper by discussing how the field of forecasting has evolved over time and proposing some directions for future research.
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