HIERARCHY DRIVEN TIME SERIES FORECASTING

A method for lightweight and efficient long sequence time-series forecasting and representation learning includes segmenting a time-series dataset from a plurality of sensors into a plurality of patches. The method further includes applying gated multilayer perceptron (MLP) mixing across different directions of the patched input time-series. The method further includes capturing local and global and interrelated correlations across the plurality of patches and within the plurality of patches. The method further includes applying a patch-time aggregated hierarchy to guide lowest-level predictions based on aggregated hierarchy signals at a patch-level. The method further includes chaining MLP-mixers in a patch length context aware hierarchy fashion to enhance time-series short and long-term correlation capture.