Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media to forecast demand by implementing an online demand prediction framework that includes a hierarchical temporal memory network (HTM) configured to learn temporal patterns representing sequences of states of time-series data collected from a set of one or more data sources representing demand and input to the HTM. In some embodiments, the HTM learns the temporal patterns using a Cortical Learning Algorithm.

Abstract: Embodiments provide methods, systems, apparatuses, and computer program products for predicting behavior. An example method includes determining a classification for a first consumer, where the classification is based on a measure of frequency of purchases by the first consumer; identifying one or more first attributes for the first consumer based on the determined classification, the one or more attributes being attributes selected for predicting the respective one or more metric associated with the first consumer; and determining, based on values for the one or more first attributes, a first prediction value that indicates a programmatically expected number of purchases by the first consumer.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting consumer behavior. In one embodiment a method is provided comprising determining a classification for a first consumer, wherein the classification is based on a measure of frequency of purchases by the first consumer; identifying one or more first attributes for the first consumer based on the determined classification, the one or more attributes being attributes selected for predicting the respective one or more metric associated with the first consumer; and determining, based on values for the one or more first attributes, a first prediction value that indicates a programmatically expected number of purchases by the first consumer.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting consumer behavior. In one embodiment a method is provided comprising determining a classification for a first consumer, wherein the classification is based on a measure of frequency of purchases by the first consumer; identifying one or more first attributes for the first consumer based on the determined classification, the one or more attributes being attributes selected for predicting the respective one or more metric associated with the first consumer; and determining, based on values for the one or more first attributes, a first prediction value that indicates a programmatically expected number of purchases by the first consumer.

Abstract: Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for predicting consumer behavior. In one embodiment a method is provided comprising determining a classification for a first consumer, wherein the classification is based on a measure of frequency of purchases by the first consumer; identifying one or more first attributes for the first consumer based on the determined classification, the one or more attributes being attributes selected for predicting the respective one or more metric associated with the first consumer; and determining, based on values for the one or more first attributes, a first prediction value that indicates a programmatically expected number of purchases by the first consumer.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media to forecast demand by implementing an online demand prediction framework that includes a hierarchical temporal memory network (HTM) configured to learn temporal patterns representing sequences of states of time-series data collected from a set of one or more data sources representing demand and input to the HTM. In some embodiments, the HTM learns the temporal patterns using a Cortical Learning Algorithm.

Abstract: In general, embodiments of the present invention provide systems, methods and computer readable media to forecast demand by implementing an online demand prediction framework that includes a hierarchical temporal memory network (HTM) configured to learn temporal patterns representing sequences of states of time-series data collected from a set of one or more data sources representing demand and input to the HTM. In some embodiments, the HTM learns the temporal patterns using a Cortical Learning Algorithm.