基于频率特征的时间序列基准,用于公平的比较评估。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhou Wu, Ruiqi Jiang
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引用次数: 2

摘要

时间序列的预测和插补在学术界和工业界都受到了广泛的关注。已经为特定的时间序列场景开发了机器学习方法;然而,很难评估某一方法在其他新案例中的有效性。从频率特征的角度出发,设计了一个时间序列预测的综合基准,以实现公平评价。采用基于有限脉冲响应滤波器的方法和问题设置模块组成的预测问题生成过程来生成NCAA2022数据集,该数据集包括16个预测问题。为了减少计算负担,将滤波器参数矩阵划分为子矩阵。引入离散傅立叶变换来分析变换结果的频率分布。此外,基线实验进一步反映了NCAA2022数据集的基准测试能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Time-series benchmarks based on frequency features for fair comparative evaluation.

Time-series benchmarks based on frequency features for fair comparative evaluation.

Time-series benchmarks based on frequency features for fair comparative evaluation.

Time-series benchmarks based on frequency features for fair comparative evaluation.

Time-series prediction and imputation receive lots of attention in academic and industrial areas. Machine learning methods have been developed for specific time-series scenarios; however, it is difficult to evaluate the effectiveness of a certain method on other new cases. In the perspective of frequency features, a comprehensive benchmark for time-series prediction is designed for fair evaluation. A prediction problem generation process, composed of the finite impulse response filter-based approach and problem setting module, is adopted to generate the NCAA2022 dataset, which includes 16 prediction problems. To reduce the computational burden, the filter parameters matrix is divided into sub-matrices. The discrete Fourier transform is introduced to analyze the frequency distribution of transformed results. In addition, a baseline experiment further reflects the benchmarking capability of NCAA2022 dataset.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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