Abstract: An embodiment includes tokenizing an input passage into an n-gram sequence of tokens. The embodiment also includes evaluating the input passage using a trained classification model that generates an output indicative of a classification of the input passage. The embodiment also includes generating a first token vector for a first token of the sequence of tokens and projecting the first token vector to a higher dimensional space, resulting in a first projected token vector. The embodiment also includes generating a first similarity score for the first projected token vector based on comparisons of the first projected token vector to each of a plurality of class representations. The embodiment also includes generating a ranked list of the tokens, wherein the generating of the ranked list includes ranking the first token among others of the tokens based on the first similarity score.

Abstract: Demand forecasting in which sets of first order differences are determined for collections of time series data for products. The first order differences identify how values within the collections of time series data change over time. The sets of first order differences are normalized to form sets of scaled first order differences such that a same scale is present between the scaled first order differences. Bins with dynamic ranges are determined for the scaled first order differences based on a distribution of the scaled first order differences; The scaled first order differences are placed into the bins to form sets of binned values in which binned values in the sets of binned values are based on numbers of scaled first order differences in the bins. The time series data are grouped into segments based on a correlation between the sets of binned values for the time series data.