A learning algorithm for time series based on statistical features

被引：2

作者：

Bassi, Saksham ^{[1
]}

Gomekar, Atharva ^{[1
]}

Murthy, A. S. Vasudeva ^{[1
]}

机构：

[1] Tata Inst Fundamental Res, Ctr Applicable Math, Bangalore, Karnataka, India

来源：

INTERNATIONAL JOURNAL OF ADVANCES IN ENGINEERING SCIENCES AND APPLIED MATHEMATICS | 2019年 / 11卷 / 03期

关键词：

Time-series forecasting; Deep learning; Statistical analysis;

D O I：

10.1007/s12572-019-00253-6

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In this study, we propose a machine learning technique for time-series data which combines statistical features and neural networks. The proposed algorithm is tested on various time series like stock prices, astronomical light curve and currency exchange rates. An implementation of reconstruction of unseen time series based on encodings learned by the neural network from the training data is proposed and tested. The predicted time series based on statistical features of past values show that the trained models were able to capture well the structure of the time-series data.

引用

页码：230 / 235

页数：6

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