1. bookVolume 6 (2021): Issue 1 (January 2021)
Journal Details
License
Format
Journal
First Published
01 Jan 2016
Publication timeframe
2 times per year
Languages
English
access type Open Access

Predicting stock high price using forecast error with recurrent neural network

Published Online: 25 May 2021
Page range: 283 - 292
Received: 24 Dec 2020
Accepted: 11 Apr 2021
Journal Details
License
Format
Journal
First Published
01 Jan 2016
Publication timeframe
2 times per year
Languages
English
Abstract

Stock price forecasting is an eye-catching research topic. In previous works, many researchers used a single method or combination of methods to make predictions. However, accurately predicting stock prices is very difficult. To improve the predicting precision, in this study, an innovative prediction approach was proposed by recurrent substitution of forecast error into the historical neural network model through three steps. According to the historical data, the initial predicted value of the next day is obtained through the neural network. Then, the prediction error of the next day is obtained through the neural network according to the historical prediction error. Finally, the initial predicted value and the prediction error are added to obtain the final predicted value of the next day. We use recurrent neural network prediction methods, such as Long Short-Term Memory Network Model and Gated Recurrent Unit, which are popular in the recent neural network study. In the simulations, the past stock prices of China from June 2010 to August 2017 are used as training data, and those from September 2017 to April 2018 are used as test data. The experimental findings demonstrate that the proposed method with forecast error gives a more accurate prediction result for the stock’s high price on the next day, which indicates that the performance of the proposed one is superior to that of the traditional models without forecast error.

Keywords

Introduction

Financial time series forecasting uses historical data of financial products to establish a predictive model to explore the price fluctuations of financial products, thus guiding investors to make rational investments. Accurate and stable financial forecasting models are crucial for investors to hedge risks and develop money-spinning investment policies. Therefore, studying the prediction of financial time series is of great significance. However, the financial market is a complicated nonlinear dynamic system affected by many elements. It is very elusive to predict financial prices depending on the information obtained.

Traditional financial time series analysis methods, such as the auto-regressive integrated moving average (ARIMA) model [1], auto-regressive conditional heteroskedasticity (ARCH) model, and generalised autoregressive conditional heteroskedasticity (GARCH) model [2, 3], are based on mathematical statistics, with the assumption of stationary assumptions, normal distribution assumptions, etc. Analysis by some well-constructed models requires strict parameters and requires superb modelling skills and rich practical experience. However, due to the many factors affecting the financial market, the financial time series data is very complex, with high noise, non-linearity, non-normal characteristics, etc. As a result of these and other related factors, the traditional analysis methods cannot realise time series analysis in the financial field.

In recent years, as information technology advances, many new methods and new ideas have been provided for financial analysis and forecasting. In the financial time series analysis circle, using data mining research methods and data-driven design models, some shortcomings of traditional time series analysis methods can be overcome by analysing and processing of large-scale data sets.

Meanwhile, a deep learning algorithm achieves tremendous progress in portrait recognition, speech recognition, automatic driving, and other fields. Among all deep learning algorithms, Recurrent Neural Network (RNN) [4, 5] can be considered an ideal financial time sequence analysis algorithm due to its natural sequence structure.

The Long Short-Term Memory Network Model (LSTM) [6,7], as a special variant of the RNN, is often used to process events with long delays or large intervals in time series data. This method has proved its importance in handwritten digit recognition, question answering systems, and speech recognition. In comparison with the traditional RNN, the LSTM model is characterised by selective memory and internal interaction of timing. This feature is very suitable for non-stationary data with the randomness of stock price series. The Gated Recurrent Unit (GRU) [8,9] is like an LSTM but with fewer parameters than LSTM. GRU has better performance on some smaller data sets than LSTM.

In this study, the initial forecast of the stock price is first performed, then the possible error is predicted and then the initial predicted price and the forecast error are combined to obtain the final predicted value. To decrease the errors from historical data, we use RNN prediction methods, such as LSTM and GRU, which are popular in recent years.

The paper is organised as follows. Section 2 presents the methodologies used in this study. The proposed model is introduced in Section 3. Section 4 introduces the experimental results. Section 5 summarises the paper.

Methodologies
Multi-Layer Perceptron

Artificial Neural Networks (ANN) [10, 11] is a research hot spot in the AI circle. It simulates the network structure of the human brain, and different network models can be constructed according to different connection methods. It is often referred to directly as the Neural Network (NN).

Multi-Layer Perceptron (MLP) [12,13] refers to a forward-structured artificial neural network. Its function is to map input vectors group to output vectors group, as shown in Figure 1. MLP can be considered to be a directed graph composed of multiple node layers, among which each node connects to the next layer. Every node besides input nodes is a neuron with non-linear activation. A supervised learning method called a back-propagation algorithm is usually used to train MLPs.

Fig. 1

An example of MLP.

In the 1980s, MLP was a popular method with various applications, including image recognition, machine translation, etc. In recent years, deep learning becomes the focus of people’s attention, and MLP attracts people’s attention again.

Recurrent Neural Network

RNN [4, 5] is an artificial neural network in which the connected nodes form a directed graph along a sequence. The RNN can use its internal state as input, enabling it to display the time series’ temporal dynamic behaviour and thus complete tasks such as handwriting recognition or speech recognition.

RNN is an easy tool with which to process sequence data. The input of the hidden layer comes from the output of both the input layer and the previously hidden layer, as shown in Figure 2. In theory, the RNN can process sequence data of any length. However, in practice, to reduce complexity, the current state is generally assumed to only relate to certain previous states.

Fig. 2

An example of RNN.

In contrast with the traditional machine learning model, hidden layer units are thoroughly equal. The hidden layer in the RNN is a time series from left to right. During the analysis, we often extend the RNN in time to get the structure shown in Figure 3.

Fig. 3

Unfolding of RNN in time domain.

Long Short-Term Memory

The LSTM [6, 7] network is an RNN that can process and predict important events with longer intervals and delays in time series. LSTM is often applied in the technology field in various ways. The systems based on LSTM can learn tasks such as translation language, speech recognition, handwriting recognition, predictive disease, and stock forecasting.

The LSTM and RNN are different in that a ‘processor’ is added to the algorithm to identify useful information in the LSTM case. The structure of this processor is called a unit. The three doors are arranged in a unit named the input gate, the forgetting gate and the output gate, as shown in Figure 4. When messages enter the LSTM network, they will be recognised under the rules. If the information follows the algorithm certification, it will be left. Otherwise, the unmatched information will be discarded through the Forgotten Gate. LSTM is very efficient in solving long-order dependency, which is highly versatile and has many possibilities.

Fig. 4

An example of LSTM unit.

A typical model of LSTM is defined as follows: ft=σ(Wf[ht-1,xt]+bf) {f_t} = \sigma \left({{W_f} \cdot \left[{{h_{t - 1}},{x_t}} \right] + {b_f}} \right) it=σ(Wi[ht-1,xt]+bi) {i_t} = \sigma \left({{W_i} \cdot \left[{{h_{t - 1}},{x_t}} \right] + {b_i}} \right) C˜t=tanh(Wc[ht-1,xt]+bc) {\tilde C_t} = \tanh \left({{W_c} \cdot \left[{{h_{t - 1}},{x_t}} \right] + {b_c}} \right) Ct=ft*Ct-1+it*C˜t {C_t} = {f_t}*{C_{t - 1}} + {i_t}*{\widetilde C_t} ot=σ(Wo[ht-1,xt]+bo) {o_t} = \sigma \left({{W_o} \cdot \left[{{h_{t - 1}},{x_t}} \right] + {b_o}} \right) ht=ot*tanh(Ct) {h_t} = {o_t}*\tanh \left({{C_t}} \right)

xt means the input vector at time t, ht means the output vector, ct means the memory cell state, it means the input gate vector, ft means the forget gate vector, ot means the output gate vector, Wi, Wf, Wo and Wc mean the weight matrices, bi, bf, bo and bc mean the bias vector and σ means activation function.

Gated Recurrent Unit (GRU)

GRU [8, 9] is a gating mechanism in a recurrent neural network. The GRU looks like an LSTM carrying two gates but with a fewer number of parameters, as shown in Figure 5. GRU’s performance in music modelling and speech signal modelling is similar to that of LSTM. GRU excels LSTM on the performance of some smaller data sets.

Fig. 5

An example of GRU unit.

A typical model of GRU is defined as follows: rt=σ(Wr[ht-1,xt]+br) {r_t} = \sigma \left({{W_r} \cdot \left[{{h_{t - 1}},{x_t}} \right] + {b_r}} \right) zt=σ(Wz[ht-1,xt]+bz) {z_t} = \sigma \left({{W_z} \cdot \left[{{h_{t - 1}},{x_t}} \right] + {b_z}} \right) h˜t=tanh(Wh[rt*ht-1,xt]+bh) {\tilde h_t} = \tanh \left({{W_h} \cdot \left[{{r_t}*{h_{t - 1}},{x_t}} \right] + {b_h}} \right) ht=(1-zt)*ht-1+zt*h˜t {h_t} = {\left({1 - {z_t}} \right)^*}{h_{t - 1}} + {z_t}*{\tilde h_t}

xt is the input vector at time t, ht is the output vector, rt is the reset gate vector, zt is the update gate vector, Wr, Wz and Wh are the weight matrices, br, bz and bh are the bias vector and σ is the activation function.

Proposed Model

Figure 6 shows the proposed model with the detailed prediction steps as given below:

From input history data {[x1, y1],. . . , [xt, yt]}, get yp’t+1 via a Neural Network method (NN1), such as LSTM, GRU or MLP.

Get the error history {e1,e2,. . . ,et} via the equation: et = yt-yp’t.

From the error history {e1,e2,. . . ,et}, get ept+1, via a Neural Network method (NN2), such as LSTM, GRU or MLP.

Get the final predictive result using the equation: ypt+1 = yp’t+1+ept+1.

Fig. 6

The proposed model.

The flow of GRU or LSTM in NN1 is shown in Figure 7. After some preliminary experiments we found this model to be suitable for our problem. GRU/LSTM layer has 30 GRU/LSTM neural units. In the dense layer, the activation function is linear. The output is used as a high price of the next day.

Fig. 7

The flow of a GRU or LSTM in NN1.

Experiments
Performance Metrics

To assess the forecasting effect of the proposed model, the results were tested by some evaluation criteria, which are a Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Mean Absolute Scaled Error (MASE), as shown in Eqs (11)(13) [14]. The RMSE, MAPE and MASE represent the difference between the values yt and ypt, where yt is the actual value of the t-th sample, ypt is the forecast value of the t-th sample and m is the sample size of the test set. Thus, the smaller their values are, the better the model performs. The predictive directionality index (Dstat) is defined by Eq. (14). Dstat indicates the consistency between the actual trend and the predicted trend, and a greater degree of consistency is indicative of a better result. RMSE=t=1m(ypt-yt)2m RMSE = \sqrt {{{\sum\limits_{t = 1}^m {{\left({{y^p}t - {y_t}} \right)}^2}} \over m}} MAPE=1m(t=1m|ytp-ytyt|)×100% MAPE = {1 \over m}\left({\sum\limits_{t = 1}^m \left| {{{y_t^p - {y_t}} \over {{y_t}}}} \right|} \right) \times 100\% MASE=1mt=1m|ytp-yt|1m-1t=2m|yt-yt-1| MASE = {1 \over m}{{\sum\limits_{t = 1}^m \left| {y_t^p - {y_t}} \right|} \over {{1 \over {m - 1}}\sum\limits_{t = 2}^m \left| {{y_t} - {y_{t - 1}}} \right|}} Dstat=1m-1t=2mat,at={1,if(yt-yt-1)(ytp-yt-1p)>00,otherwise {\rm{Dstat}} = {1 \over {m - 1}}\sum\limits_{t = 2}^m {a_t},{a_t} = \left\{{\matrix{{1,if\left({{y_t} - {y_{t - 1}}} \right)\left({y_t^p - y_{t - 1}^p} \right) > 0} \hfill \cr {0,\quad {\rm{otherwise}}} \hfill \cr}} \right.

Experiment Results 1

The Shanghai Composite Index (000001.SH) of China is used as experimental data. The daily data includes six variables, such as opening price, high price, low price, closing price, change of price and a number of transactions.

The data from the previous few days is used as input data, and the high price of the next date is used as the output data. In the emulations, the stock prices from June 2010 to August 2017 are used as training data (as shown in Figure 8) and those from September 2017 to April 2018 are used as test data (as shown in Figure 9).

Fig. 8

The high price of training data.

Fig. 9

The high price of test data.

The experiments were implemented in Python 3.5.3; and measured on a computer with Intel(R) Core(TM) i7-6700 CPU at 3.40 GHz, 8.0 GB RAM, and Microsoft Windows10 Professional 64 bits.

Tables 13 show the experiment results. We tested each sample for more than 10 times independently; we obtained the average values of 10 individuals. “Time” refers to the mean test time; the unit of time is in seconds.

The experiment results (NN1: LSTM, DATA SET: 000001.SH).

Models Time RMSE MAPE MASE Dstat
LSTM 0.102 27.243 0.643 1.204 0.653
LSTM,GRU 0.188 22.195 0.481 0.900 0.673
LSTM,LSTM 0.207 22.225 0.483 0.903 0.667
LSTM,MLP 0.125 23.570 0.526 0.984 0.669

The experiment results (NN1: GRU, DATA SET: 000001.SH).

Models Time RMSE MAPE MASE Dstat
GRU 0.090 28.676 0.683 1.281 0.693
GRU,GRU 0.178 21.269 0.449 0.837 0.713
GRU,LSTM 0.193 21.319 0.450 0.840 0.703
GRU,MLP 0.112 23.559 0.517 0.968 0.710

The experiment results (NN1: MLP, DATA SET: 000001.SH).

Models Time RMSE MAPE MASE Dstat
MLP 0.023 29.953 0.729 1.368 0.640
MLP,GRU 0.114 23.964 0.521 0.973 0.665
MLP,LSTM 0.128 23.328 0.509 0.950 0.658
MLP,MLP 0.045 24.727 0.560 1.047 0.657

In Table 1, the NN1 is LSTM, and the NN2 is LSTM, GRU or MLP, respectively. The values of the error-index (RMSE, MAPE, MASE) of proposed models are smaller than that of LSTM. The Dstat value of the proposed models is larger than that of LSTM. Thus, the predicting effect of the proposed method excels that of LSTM. However, the running time of the proposed methods is longer than that of LSTM.

In Table 2, the NN1 is GRU, and the NN2 is LSTM, GRU or MLP, respectively. The values of the error-index of the proposed models are smaller than that of GRU. The Dstat values of the proposed models are larger than those of GRU. However, the running time of the proposed methods is longer than that of GRU.

In Table 3, the NN1 is MLP, and the NN2 is LSTM, GRU or MLP, respectively. The values of the error-index of the proposed models are smaller than that of MLP. The Dstat values of the proposed models are larger than that of MLP. However, the running time of the proposed methods is longer than that of MLP.

Tables 13 show that the performance of the proposed method (NN1:GRU, NN2:GRU) is better than others, because GRU has better performance on some smaller data sets than LSTM.

Figure 10 shows a prediction sample of the proposed method (NN1:GRU, NN2:GRU). The blue zigzag line is the actual trend; the red zigzag line is the predicted trend. The proposed method can give a precise prediction.

Fig. 10

A prediction sample of the proposed model (NN1:GRU, NN2:GRU, DATA SET: 000001.SH).

Experiment Results 2

The Shenzhen Composite Index (399001.SZ) of China is used as experimental data. In the simulations, the stock prices from May 2011 to July 2018 are used as training data, and those from August 2018 to March 2019 are used as test data. The Standard Deviation of data set 399001.SZ is bigger than that of data set 000001.SH, as shown in Table 4.

The standard deviation of experimental data.

Data set Training Test
000001.SH 595.8 106.6
399001.SZ 1773.1 749.3

In Table 5, the performance of the proposed method (NN1:GRU, NN2:GRU) is also better than others. On the other hand, the values of the error-index (RMSE, MAPE, MASE) in Table 5 are bigger than those in Table 2 because the Standard Deviation values of data set 399001.SZ are bigger than that of data set 000001.SH.

The experiment results (NN1: GRU, DATA SET: 399001.SZ).

Models Time RMSE MAPE MASE Dstat
GRU 0.090 127.056 1.143 1.048 0.621
GRU,GRU 0.177 115.375 1.021 0.934 0.621
GRU,LSTM 0.109 126.870 1.092 1.007 0.620
GRU,MLP 0.023 120.234 1.050 0.961 0.615
Conclusion

Stock price forecasting is a research hotspot. Accurate prediction of stock prices is difficult. In the proposed model, the initial forecast of the stock price is first performed, then the possible error is predicted and then the initial predicted price and the forecast error are combined to obtain the final predicted value. In the experiment, we used GRU, LSTM and MLP methods in combination. Our proposed model has been proven to be effective based on considering the results of our study, and the proposed method (NN1:GRU, NN2:GRU) shows its superior performance because GRU has better performance on some smaller data sets than LSTM.

In future work, we will further study more parameter settings of the neural networks, such as the number of layers in the network, the nodes number in each layer, the activation function, etc.. Shortening the training time is another problem that needs to be solved.

Fig. 1

An example of MLP.
An example of MLP.

Fig. 2

An example of RNN.
An example of RNN.

Fig. 3

Unfolding of RNN in time domain.
Unfolding of RNN in time domain.

Fig. 4

An example of LSTM unit.
An example of LSTM unit.

Fig. 5

An example of GRU unit.
An example of GRU unit.

Fig. 6

The proposed model.
The proposed model.

Fig. 7

The flow of a GRU or LSTM in NN1.
The flow of a GRU or LSTM in NN1.

Fig. 8

The high price of training data.
The high price of training data.

Fig. 9

The high price of test data.
The high price of test data.

Fig. 10

A prediction sample of the proposed model (NN1:GRU, NN2:GRU, DATA SET: 000001.SH).
A prediction sample of the proposed model (NN1:GRU, NN2:GRU, DATA SET: 000001.SH).

The experiment results (NN1: GRU, DATA SET: 000001.SH).

Models Time RMSE MAPE MASE Dstat
GRU 0.090 28.676 0.683 1.281 0.693
GRU,GRU 0.178 21.269 0.449 0.837 0.713
GRU,LSTM 0.193 21.319 0.450 0.840 0.703
GRU,MLP 0.112 23.559 0.517 0.968 0.710

The standard deviation of experimental data.

Data set Training Test
000001.SH 595.8 106.6
399001.SZ 1773.1 749.3

The experiment results (NN1: GRU, DATA SET: 399001.SZ).

Models Time RMSE MAPE MASE Dstat
GRU 0.090 127.056 1.143 1.048 0.621
GRU,GRU 0.177 115.375 1.021 0.934 0.621
GRU,LSTM 0.109 126.870 1.092 1.007 0.620
GRU,MLP 0.023 120.234 1.050 0.961 0.615

The experiment results (NN1: LSTM, DATA SET: 000001.SH).

Models Time RMSE MAPE MASE Dstat
LSTM 0.102 27.243 0.643 1.204 0.653
LSTM,GRU 0.188 22.195 0.481 0.900 0.673
LSTM,LSTM 0.207 22.225 0.483 0.903 0.667
LSTM,MLP 0.125 23.570 0.526 0.984 0.669

The experiment results (NN1: MLP, DATA SET: 000001.SH).

Models Time RMSE MAPE MASE Dstat
MLP 0.023 29.953 0.729 1.368 0.640
MLP,GRU 0.114 23.964 0.521 0.973 0.665
MLP,LSTM 0.128 23.328 0.509 0.950 0.658
MLP,MLP 0.045 24.727 0.560 1.047 0.657

Aasim, S.N. Singh, and A. Mohapatra, Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting, Renewable Energy, vol. 136, no. 6, pp. 758–768, 2019. AasimS.N. Singh MohapatraA. Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting Renewable Energy 136 6 758 768 2019 Search in Google Scholar

Qi liu, Guanlan Zhang, Shahzad Ali, Xiaopeng Wang, Guodong Wang, Zhenkuan Pan, and Jiahua Zhang, SPI-based drought simulation and prediction using ARMA-GARCH model, Applied Mathematics and Computation, vol. 355, no. 8, pp. 96–107, 2019. LiuQi ZhangGuanlan AliShahzad WangXiaopeng WangGuodong PanZhenkuan ZhangJiahua SPI-based drought simulation and prediction using ARMA-GARCH model Applied Mathematics and Computation 355 8 96 107 2019 Search in Google Scholar

Clément Cerovecki, Christian Francq, Siegfried Hörmann, and Jean-Michel Zakoïan, Functional GARCH models: The quasi-likelihood approach and its applications, Journal of Econometrics, vol. 209, no. 2, pp. 353–375, 2019. CeroveckiClément FrancqChristian HörmannSiegfried ZakoïanJean-Michel Functional GARCH models: The quasi-likelihood approach and its applications Journal of Econometrics 209 2 353 375 2019 Search in Google Scholar

S. Sivakumar and S. Sivakumar, Marginally Stable Triangular Recurrent Neural Network Architecture for Time Series Prediction, IEEE Transactions on Cybernetics, vol. 48, no. 10, pp. 2836–2850, 2018. SivakumarS. SivakumarS. Marginally Stable Triangular Recurrent Neural Network Architecture for Time Series Prediction IEEE Transactions on Cybernetics 48 10 2836 2850 2018 Search in Google Scholar

P. Liu, Z. Zeng and J. Wang, Multistability of Recurrent Neural Networks With Nonmonotonic Activation Functions and Unbounded Time-Varying Delays, IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 7, pp. 3000–3010, 2018. LiuP. ZengZ. WangJ. Multistability of Recurrent Neural Networks With Nonmonotonic Activation Functions and Unbounded Time-Varying Delays IEEE Transactions on Neural Networks and Learning Systems 29 7 3000 3010 2018 Search in Google Scholar

W. Lee, K. Kim, J. Park, J. Kim and Y. Kim, Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks, IEEE Access, vol. 6, no. 11, pp. 73068–73080, 2018. LeeW. KimK. ParkJ. KimJ. KimY. Forecasting Solar Power Using Long-Short Term Memory and Convolutional Neural Networks IEEE Access 6 11 73068 73080 2018 Search in Google Scholar

Q. Zhang, H. Wang, J. Dong, G. Zhong and X. Sun, Prediction of Sea Surface Temperature Using Long Short-Term Memory, IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 10, pp. 1745–1749, 2017. ZhangQ. WangH. DongJ. ZhongG. SunX. Prediction of Sea Surface Temperature Using Long Short-Term Memory IEEE Geoscience and Remote Sensing Letters 14 10 1745 1749 2017 Search in Google Scholar

M. Ravanelli, P. Brakel, M. Omologo and Y. Bengio, Light Gated Recurrent Units for Speech Recognition, IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 2, no. 2, pp. 92–102, 2018. RavanelliM. BrakelP. OmologoM. BengioY. Light Gated Recurrent Units for Speech Recognition IEEE Transactions on Emerging Topics in Computational Intelligence 2 2 92 102 2018 Search in Google Scholar

R. Zhao, D. Wang, R. Yan, K. Mao, F. Shen and J. Wang, Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks, IEEE Transactions on Industrial Electronics, vol. 65, no. 2, pp. 1539–1548, 2018. ZhaoR. WangD. YanR. MaoK. ShenF. WangJ. Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks IEEE Transactions on Industrial Electronics 65 2 1539 1548 2018 Search in Google Scholar

A.Y. Alanis, Electricity Prices Forecasting using Artificial Neural Networks, IEEE Latin America Transactions, vol. 16, no.1, pp. 105–111, 2018. AlanisA.Y. Electricity Prices Forecasting using Artificial Neural Networks IEEE Latin America Transactions 16 1 105 111 2018 Search in Google Scholar

L. Chen, Z. Qiao, M. Wang, C. Wang, R. Du and H. E. Stanley, Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market, IEEE Access, vol. 6, no. 8, pp. 48625–48633, 2018. ChenL. QiaoZ. WangM. WangC. DuR. StanleyH. E. Which Artificial Intelligence Algorithm Better Predicts the Chinese Stock Market IEEE Access 6 8 48625 48633 2018 Search in Google Scholar

J. Tang, C. Deng and G. Huang, Extreme Learning Machine for Multilayer Perceptron, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 4, pp. 809–821, 2016. TangJ. DengC. HuangG. Extreme Learning Machine for Multilayer Perceptron IEEE Transactions on Neural Networks and Learning Systems 27 4 809 821 2016 Search in Google Scholar

L. Grippo, A. Manno and M. Sciandrone, Decomposition Techniques for Multilayer Perceptron Training, IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 11, pp. 2146–2159, 2016. GrippoL. MannoA. SciandroneM. Decomposition Techniques for Multilayer Perceptron Training IEEE Transactions on Neural Networks and Learning Systems 27 11 2146 2159 2016 Search in Google Scholar

Lili Mo, Ling Xie, Xiaoyi Jiang, Geer Teng, Lixiang Xu and Jin Xiao, GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries, Applied Soft Computing, vol. 62, no. 1, pp. 478–490, 2018. MoLili XieLing JiangXiaoyi TengGeer XuLixiang XiaoJin GMDH-based hybrid model for container throughput forecasting: Selective combination forecasting in nonlinear subseries Applied Soft Computing 62 1 478 490 2018 Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo