A deep residual compensation extreme learning machine and applications

Abstract

The extreme learning machine (ELM) is a type of machine learning algorithm for training a single hidden layer feedforward neural network. Randomly initializing the weight between the input layer and the hidden layer and the threshold of each hidden layer neuron, the weight matrix of the hidden layer can be calculated by the least squares method. The efficient learning ability in ELM makes it widely applicable in classification, regression, and more. However, owing to some unutilized information in the residual, there are relatively huge prediction errors involving ELM. In this paper, a deep residual compensation extreme learning machine model (DRC‐ELM) of multilayer structures applied to regression is presented. The first layer is the basic ELM layer, which helps in obtaining an approximation of the objective function by learning the characteristics of the sample. The other layers are the residual compensation layers in which the learned residual is corrected layer by layer to the predicted value obtained in the previous layer by constructing a feature mapping between the input layer and the output of the upper layer. This model is applied to two practical problems: gold price forecasting and airfoil self‐noise prediction. We used the DRC‐ELM with 50, 100, and 200 residual compensation layers respectively for experiments, which show that DRC‐ELM does better in generalization and robustness than classical ELM, improved ELM models such as GA‐RELM and OS‐ELM, and other traditional machine learning algorithms such as support vector machine (SVM) and back‐propagation neural network (BPNN).

Forecasting mid-price movement of Bitcoin futures using machine learning

“…The second is based on the development of cryptocurrencies with an emphasis on futures pricing behaviour, while finally, the third area through which we develop our work is based on several pieces that have examined the predictability of cryptocurrency spot prices. Primarily, machine learning has been used across a variety of areas such as that of stock markets (Wittkemper and Steiner 1996;Ntakaris et al 2018;Sirignano 2019;Huck 2019;Sirignano and Cont 2019;Huang and Liu 2020;Philip 2020); currency markets during crises (El Shazly and El Shazly 1999;Zimmermann et al 2001;Auld and Linton 2019); energy markets such as West Texas Intermediate (Chai et al 2018), crude oil markets (Fan et al 2016), Cushing oil and gasoline markets (Wang et al 2018), gold markets (Chen et al 2020); gas markets (Ftiti et al 2020), agricultural futures (Fang et al 2020); copper markets (Sánchez Lasheras et al 2015); and coal markets (Matyjaszek et al 2019;Alameer et al 2020); cryptocurrency spot markets Chowdhury et al 2020;Chen et al 2021) options markets (Lajbcygier 2004;De Spiegeleer et al 2018); and futures markets (Kim et al 2020).…”

A novel hybrid fine particulate matter (PM2.5) forecasting and its further application system: Case studies in China

“…The ELM (Huang et al, 2006) has been popular utilized in many fields owing to the fast-learning speed and random initialization of parameters (Chen et al, 2020). Supposing N observations: (x t , y t ),t = 1,2,…,N, thereinto, x t R n and y t R m are the input and output patterns.…”

A Hybrid Method Based on Extreme Learning Machine and Wavelet Transform Denoising for Stock Prediction

“…Due to efficient learning ability of ELM, it is widely used in classification, regression problems, etc. [ 14 , 15 ]. In addition to being used for traditional classification and regression tasks, ELM has recently been extended for clustering, feature selection, and representation learning [ 16 ].…”