Abstract: The disclosure relates to a method, for reporting an anomaly in a telecommunications node. The method comprises obtaining a measurement of a key performance indicator (KPI) of the telecommunication node. The method comprises, upon receiving the measurement of the KPI, updating coefficients of a polynomial function. The method comprises, based on the updated coefficients of the polynomial function, computing an expected measurement of the KPI coefficient. The method comprises computing a confidence band for the expected measurement of the KPI. The method comprises reporting the anomaly when the measurement of the KPI is outside of the confidence band. The disclosure also relates to a method for forecasting a plurality of expected measurements for a key performance indicator (KPI) in a telecommunications node, as well as to apparatuses for executing these methods.

Abstract: Each neural element of a column-structured recurrent neural network generates an output from input data and recurrent data provided from a context layer of a corresponding column. One or more candidates for an estimated value is obtained, and an occurrence probability is computed using an internal state by solving an estimation equation determined by the internal state output from the neural network. A candidate having the highest occurrence probability is an estimated value for unknown data. Thus, the internal state of the recurrent neural network is explicitly associated with the estimated value for data, and a data change can be efficiently estimated.