Search Results for author:
Found 4 papers, 1 papers with code
An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet.
Temporal Overdrive Recurrent Neural Network
In this work we present a novel recurrent neural network architecture designed to model systems characterized by multiple characteristic timescales in their dynamics.
Data-driven detrending of nonstationary fractal time series with echo state networks
We assume that such a dynamical process is predictable to a certain degree by means of a class of recurrent networks called Echo State Network (ESN), which are capable to model a generic dynamical process.
An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure yielding compact and separated clusters.
