Predicting time series has significant practical applications over different disciplines.Here,we propose an Anticipated Leaing Machine (ALM) to achieve precise future-state predictions based on short-term but high-dimensional data.From non-linear dynamical systems theory,we show that ALM can transform recent correlation/spatial information of high-dimensional variables into future dynamical/temporal information of any target variable,thereby overcoming the small-sample problem and achieving multistep-ahead predictions.Since the training samples generated from high-dimensional data also include information of the unknown future values of the target variable,it is called anticipated leaing.Extensive experiments on real-world data demonstrate significantly superior performances of ALM over all of the existing 12 methods.In contrast to traditional statistics-based machine leaing,ALM is based on non-linear dynamics,thus opening a new way for dynamics-based machine leaing.