期刊
INTERNATIONAL JOURNAL OF BIFURCATION AND CHAOS
卷 24, 期 12, 页码 -出版社
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S021812741430033X
关键词
Prediction; short-term; high-dimensional data
资金
- FIRST program from JSPS
- CSTP
- Strategic Priority Research Program of the Chinese Academy of Sciences [XDB13040700]
- National Natural Science Foundation of China [61134013, 91029301, 11301366, 91230204]
The prediction of future values of time series is a challenging task in many fields. In particular, making prediction based on short-term data is believed to be difficult. Here, we propose a method to predict systems' low-dimensional dynamics from high-dimensional but short-term data. Intuitively, it can be considered as a transformation from the inter-variable information of the observed high-dimensional data into the corresponding low-dimensional but long-term data, thereby equivalent to prediction of time series data. Technically, this method can be viewed as an inverse implementation of delayed embedding reconstruction. Both methods and algorithms are developed. To demonstrate the effectiveness of the theoretical result, benchmark examples and real-world problems from various fields are studied.
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