Asymptotic normality of the local linear estimation of the conditional density for functional time-series data

A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model

“…Backpropagation neural network (BPNN) is the first widely used ANN method for STLF [17]. A combined model, which used the back-propagation neural network (BPNN) with the multilabel algorithm based on K-nearest neighbor (K-NN) and K-means, was proposed for STLF in [18]; however, BPNN is a feedforward neural network, and it cannot well learn time sequence data in the power system [19]. In order to efficiently process the time sequence data, such as holiday, weather, and temperature information in the power system.Recurrent neural network (RNN) [20], a kind of neural network which is specific for processing sequence data, is widely used for STLF [21].…”

Rate of the Almost Sure Convergence of a Generalized Regression Estimate Based on Truncated and Functional Data

“…For example, in [9] the uniform almost-complete convergence of the local linear conditional quantile estimator was established, while in [8] the case of a generalized regression function with functional dependent data was considered. The asymptotic normality of the local linear estimator of the conditional density for functional time series data was studied in [12] and both the pointwise and the uniform almost complete convergences, of a generalized regression estimate, were investigated in [7]. All these studies were carried in the case of complete data, however in practice, one or more truncation variables may interfere with the variable of interest and prevent its observation in a complete manner.…”