A weighted EMD-based prediction model based on TOPSIS and feed forward neural network for noised time series

2017 ◽  
Vol 132 ◽  
pp. 167-178 ◽  
Author(s):  
Wang Jun ◽  
Tang Lingyu ◽  
Luo Yuyan ◽  
Ge Peng
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Model Based

Stock Price Prediction Model Based on IWO Neural Network and its Applications

2014 ◽  
Vol 989-994 ◽  
pp. 1635-1640
Author(s):  
Hong Liu ◽  
Xiao Yan Lv
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Stock Price ◽  
Steepest Descent Method ◽  
Local Optimum ◽  
Back Propagation Algorithm ◽  
Feed Forward ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Model Based

In view of the deficiency of the standard back-propagation algorithm based on steepest descent method, a new kind of optimization strategy called invasive weed optimization (IWO) algorithm is introduced into the training process of feed-forward neural networks, and then a prediction model based on IWO feed-forward neural network (IWO-NN) is given. By the dynamic adjustment of standard deviation of the distribution of offspring individuals in IWO, the local convergence speed of networks is improved and the defect of trapping into a local optimum is reduced. By the empirical study of stock price prediction in Sany Heavy Industry, the results show that this method has better global astringency, robustness, and it is insensitive to initial values.

scholarly journals PERAMALAN INFLOW UANG KARTAL BANK INDONESIA KPW TASIKMALAYA JAWA BARAT DENGAN METODE KLASIK DAN MODERN

2020 ◽  
Vol 8 (2) ◽  
pp. 166
Author(s):  
Nur Silviyah Rahmi
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Multilayer Perceptron ◽  
Feed Forward Neural Network ◽  
Feed Forward

Ketersediaan uang kartal di Bank Indonesia (BI) dapat ditinjau melalui arus keluar masuknya uang kartal yang disebut dengan istilah inflow. Banyaknya uang yang beredar di masyarakat akan berpengaruh pada kondisi perekonomian suatu negara, sehingga Bank Indonesia (BI) menyusun perencanaan kebutuhan uang rupiah. Penelitian ini bertujuan untuk meramalkan inflow uang kartal di KPw Bank Indonesia (BI) Tasikmalaya dengan menggunakan pemodelan ARIMA, ARIMAX, Metode Dekomposisi, Metode Winter’s, MLP (Multilayer Perceptron) atau FFNN (Feed Forward Neural Network), Regresi Time Series, Metode Naïve dan Model Hybrid. Dari delapan metode runtun waktu tersebut baik klasik maupun modern akan dicari metode mana yang memberikan hasil akurasi ramalan yang terbaik dengan kriteria RMSE, MAPE dan MAD. Kesimpulan yang dihasilkan yaitu Hybrid ARIMA-NN yang merupakan gabungan dari model ARIMA dengan neural network tidak menjamin kinerja hasil peramalan yang lebih baik. Seperti yang disebutkan dalam hasil M3 Competition, semakin kompleks metode yang digunakan belum tentu metode tersebut menghasilkan akurasi yang lebih baik dibandingkan metode sederhana (klasik). Pada ramalan data inflow KPw BI Tasikmalaya Jawa Barat ini, menghasilkan kesimpulan bahwa metode regresi time series memiliki nilai kriteria pemodelan paling kecil dibandingkan dengan metode lainnya.

scholarly journals Ultra-Short-Term Photovoltaic Power Prediction Model Based on the Localized Emotion Reconstruction Emotional Neural Network

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2857 ◽  
Author(s):  
Yufei Wang ◽  
Li Zhu ◽  
Hua Xue
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Prediction Model ◽  
Prediction Models ◽  
Short Term Memory ◽  
Single Step ◽  
Power Prediction ◽  
Short Term ◽  
Model Based ◽  
Emotional Neural Network

Due to the intermittency and randomness of photovoltaic (PV) power, the PV power prediction accuracy of the traditional data-driven prediction models is difficult to improve. A prediction model based on the localized emotion reconstruction emotional neural network (LERENN) is proposed, which is motivated by chaos theory and the neuropsychological theory of emotion. Firstly, the chaotic nonlinear dynamics approach is used to draw the hidden characteristics of PV power time series, and the single-step cyclic rolling localized prediction mechanism is derived. Secondly, in order to establish the correlation between the prediction model and the specific characteristics of PV power time series, the extended signal and emotional parameters are reconstructed with a relatively certain local basis. Finally, the proposed prediction model is trained and tested for single-step and three-step prediction using the actual measured data. Compared with the prediction model based on the long short-term memory (LSTM) neural network, limbic-based artificial emotional neural network (LiAENN), the back propagation neural network (BPNN), and the persistence model (PM), numerical results show that the proposed prediction model achieves better accuracy and better detection of ramp events for different weather conditions when only using PV power data.

Stock Price Prediction Model Based on BA Neural Network and its Applications

2014 ◽  
Vol 989-994 ◽  
pp. 1646-1651 ◽  
Author(s):  
Xiao Yan Lv ◽  
Si Long Sun ◽  
Hong Liu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Stock Price ◽  
Frequency Tuning ◽  
Steepest Descent Method ◽  
Feed Forward ◽  
Stock Price Prediction ◽  
Price Prediction ◽  
Model Based ◽  
Intensive Exploitation

In view of the deficiency of the basic back-propagation (BP) algorithm based on steepest descent method. Bat algorithm (BA) in intelligent optimization is introduced into the training process of feed-forward neural networks, capturing the optimal solution of the objective function with a small population size and less number of iterations, and a prediction model based on BA feed-forward neural network (BA-NN) is given. By the empirical study of stock price prediction in Sany Heavy Industry, the results show that this method has advantages of frequency tuning and dynamic control of exploration and exploitation by automatic switching to intensive exploitation if necessary.

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