Multi-step-ahead Prediction from Short-term Data by Delay-embedding-based Forecast Machine. (arXiv:2005.07842v1 [eess.SP])

Making accurate multi-step-ahead prediction for a complex system is a
challenge for many practical applications, especially when only short-term
time-series data are available. In this work, we proposed a novel framework,
Delay-Embedding-based Forecast Machine (DEFM), to predict the future values of
a target variable in an accurate and multi-step-ahead manner based on the
high-dimensional short-term measurements. With a three-module spatiotemporal
architecture, DEFM leverages deep learning to effectively extract both the
spatially and sequentially associated information from the short-term dynamics
even with time-varying parameters or additive noise. Being trained through a
self-supervised scheme, DEFM well fits a nonlinear transformation that maps
from the observed high-dimensional information to the delay embeddings of a
target variable, thus predicting the future information. The effectiveness and
accuracy of DEFM is demonstrated by applications on both representative models
and six real-world datasets. The comparison with four traditional prediction
methods exhibits the superiority and robustness of DEFM.

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