A widely agreed upon definition of time series causality inference, established in the seminal 1969 article of Clive Granger (1969), is based on the relative ability of the histor...
We present a hybrid method to turn off-the-shelf information retrieval (IR) systems into future event predictors. Given a query, a time series model is trained on the publication...
Background: Modelling of time series data should not be an approximation of input data profiles, but rather be able to detect and evaluate dynamical changes in the time series dat...
Ryoko Morioka, Shigehiko Kanaya, Masami Y. Hirai, ...
A common technique to deploy linear prediction to nonstationary signals is time segmentation and local analysis. In [1], the temporal changes of linear prediction coefficients (L...
Abstract--This study proposes an efficient self-evolving evolutionary learning algorithm (SEELA) for neurofuzzy inference systems (NFISs). The major feature of the proposed SEELA i...