ePAMeT: evolving predictive associative memories for time series

Associative memories (AM) are at the core of human intelligence and learning systems. While there have been some neural network AM developed for vector-based data such as images, current machine learning methods, including deep neural networks, do not allow for training a model on time series data and recalling it on a subset of variables measured over a shorter time window. They also do not support further incremental training of the model on new temporal data and new variables. This paper introduces a new framework and method for the creation of evolving predictive associative memories for time series, abbreviated here as ePAMeT. The method is based on spiking neural networks (SNN). ePAMeT introduces significant adaptability in handling time series data with reduced or newly introduced features. This model maintains high accuracy and explainability, offering substantial improvements over traditional methods in dynamic and uncertain environments. First, an SNN model is trained on multiple time series using all available variables measured at a full-time length, and then the model is recalled on subsets of variables at a shorter time measurement without compromising predictive accuracy. Using a shorter time for recall makes early prediction of events possible. The SNN model can be further adapted/evolved on new data without pre-training the model on the old data, even using new variables. This is possible due to the evolving connectivity of the SNN model. A dynamic graph is extracted from the SNN model to capture dynamic interactions between the used temporal variables at any time during the evolution of the model, which constitutes strong explainability and a generation of new knowledge. The method is illustrated on original financial time series data, but it is applicable to many other domain areas as discussed. The proposed method has advantages over traditional machine learning methods in terms of evolvability, explainability, knowledge discovery, and using partial information of both the number of variables and their time length for the recall of the model on new data. The proposed framework opens the field for creating new types of evolvable time series prediction models. Future developments are discussed.