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In recent years, deep-learning-based approaches have been introduced tosolving time-series forecasting-related problems. These novel methods havedemonstrated impressive performance in univariate and low-dimensionalmultivariate time-series forecasting tasks. However, when these novel methodsare used to handle high-dimensional multivariate forecasting problems, theirperformance is highly restricted by a practical training time and a reasonableGPU memory configuration. In this paper, inspired by a change of basis in theHilbert space, we propose a flexible data feature extraction technique thatexcels in high-dimensional multivariate forecasting tasks. Our approach wasoriginally developed for the National Science Foundation (NSF) Algorithms forThreat Detection (ATD) 2022 Challenge. Implemented using the attentionmechanism and Convolutional Neural Networks (CNN) architecture, our methoddemonstrates great performance and compatibility. Our models trained on theGDELT Dataset finished 1st and 2nd places in the ATD sprint series and holdpromise for other datasets for time series forecasting.

cs-net: structural approach to time-series forecasting for high-dimensional feature space data with limited observations