计算机科学
水准点(测量)
残余物
深度学习
人工智能
单变量
系列(地层学)
时间序列
人工神经网络
领域(数学分析)
机器学习
航程(航空)
建筑
数据挖掘
算法
多元统计
数学
工程类
艺术
地理
视觉艺术
古生物学
航空航天工程
数学分析
生物
大地测量学
作者
Boris N. Oreshkin,Dmitri Carpov,Nicolas Chapados,Yoshua Bengio
出处
期刊:Cornell University - arXiv
日期:2019-01-01
被引量:399
标识
DOI:10.48550/arxiv.1905.10437
摘要
We focus on solving the univariate times series point forecasting problem using deep learning. We propose a deep neural architecture based on backward and forward residual links and a very deep stack of fully-connected layers. The architecture has a number of desirable properties, being interpretable, applicable without modification to a wide array of target domains, and fast to train. We test the proposed architecture on several well-known datasets, including M3, M4 and TOURISM competition datasets containing time series from diverse domains. We demonstrate state-of-the-art performance for two configurations of N-BEATS for all the datasets, improving forecast accuracy by 11% over a statistical benchmark and by 3% over last year's winner of the M4 competition, a domain-adjusted hand-crafted hybrid between neural network and statistical time series models. The first configuration of our model does not employ any time-series-specific components and its performance on heterogeneous datasets strongly suggests that, contrarily to received wisdom, deep learning primitives such as residual blocks are by themselves sufficient to solve a wide range of forecasting problems. Finally, we demonstrate how the proposed architecture can be augmented to provide outputs that are interpretable without considerable loss in accuracy.
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