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• W2895043543 abstract "Significance Making accurate forecast or prediction is a challenging task in the big data era, in particular for those datasets involving high-dimensional variables but short-term time series points, and these datasets are omnipresent in many fields. In this work, a model-free framework, named as “randomly distributed embedding” (RDE), is proposed to accurately predict future dynamics based on such short-term but high-dimensional data. The RDE framework creates the distribution information from the interactions among high-dimensional variables to compensate for the lack of time points in real applications. Instead of roughly predicting a single trial of future values, this framework achieves the accurate prediction by using the distribution information." @default.
• W2895043543 created "2018-10-12" @default.
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• W2895043543 date "2018-10-08" @default.
• W2895043543 modified "2023-10-14" @default.
• W2895043543 title "Randomly distributed embedding making short-term high-dimensional data predictable" @default.
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• W2895043543 doi "https://doi.org/10.1073/pnas.1802987115" @default.
• W2895043543 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6205453" @default.
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• W2895043543 hasPublicationYear "2018" @default.
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