A system receives transaction data over time, and creates structured data based on the received transaction data. Purchase transactions that are associated with a purchase category are identified in the structured data and labeled. A recurrent neural network such as a long short-term memory (LSTM) network, in particular, a k-LSTM architecture using weighted averages to update hidden states and cell states, is trained to build a model. The model is used to predict the likelihood of a purchase transaction.