{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T10:55:21Z","timestamp":1768560921060,"version":"3.49.0"},"reference-count":36,"publisher":"World Scientific Pub Co Pte Ltd","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Bifurcation Chaos"],"published-print":{"date-parts":[[2014,12]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1142\/s021812741430033x","type":"journal-article","created":{"date-parts":[[2015,1,5]],"date-time":"2015-01-05T06:43:47Z","timestamp":1420440227000},"page":"1430033","source":"Crossref","is-referenced-by-count":28,"title":["Predicting Time Series from Short-Term High-Dimensional Data"],"prefix":"10.1142","volume":"24","author":[{"given":"Huanfei","family":"Ma","sequence":"first","affiliation":[{"name":"Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"},{"name":"School of Mathematical Sciences, Soochow University, Suzhou 215006, P. R. China"}]},{"given":"Tianshou","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computational Science, Sun Yat-Sen University, Guangzhou 510275, P. R. China"}]},{"given":"Kazuyuki","family":"Aihara","sequence":"additional","affiliation":[{"name":"Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial Science, The University of Tokyo, Tokyo 153-8505, Japan"}]},{"given":"Luonan","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, P. R. 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