Stock Price Range Forecast via a Recurrent Neural Network Based on the Zero-Crossing Rate Approach

2019 ◽  
Author(s):  
Yu-Fei Lin ◽  
Yeong-Luh Ueng ◽  
Wei-Ho Chung ◽  
Tzu-Ming Huang
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Stock Price ◽  
Zero Crossing ◽  
Price Range ◽  
Zero Crossing Rate

scholarly journals KLASIFIKASI MUSIK MENGGUNAKAN POLYNOMIAL NEURAL NETWORK

2017 ◽  
Vol 3 (2) ◽  
pp. 94
Author(s):  
Prisca Pakan ◽  
Rocky Yefrenes Dillak
Keyword(s):  
Neural Network ◽  
Zero Crossing ◽  
Spectral Centroid ◽  
Polynomial Neural Network ◽  
Zero Crossing Rate

Penelitian ini bertujuan mengembangkan suatu metode yang dapat digunakan untuk melakukanklasifikasi terhadap jenis musik berdasarkan file audio dengan format wav menggunakan algoritmaRidge Polynomial Neural Network (RPNN). Pengklasifikasian file audio ke dalam suatu kelompokatau kelas, memerlukan ciri atau fitur dari file audio tersebut. Metode ekstrak fitur yang digunakanuntuk memperoleh ciri atau fitur dari file yang dimaksud adalah Spectral Centroid (SC), SortTime Energy (STE) dan Zero Crossing Rate (ZCR) yang diturunkan dalam domain waktu (timedomain) yang merupakan salah satu komponen data audio. Berdasarkan hasil dari penelitian inimenunjukkan bahwa pendekatan yang diusulkan mampu melakukan klasifikasi terhadap jenis musikberdasarkan file audio berformat wav dengan akurasi sebesar 90%

scholarly journals Study on the prediction of stock price based on the associated network model of LSTM

2019 ◽  
Vol 11 (6) ◽  
pp. 1307-1317 ◽  
Author(s):  
Guangyu Ding ◽  
Liangxi Qin
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Stock Market ◽  
Statistical Methods ◽  
Network Model ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Stock Price ◽  
Artificial Intelligence Methods ◽  
Deep Recurrent Neural Network

AbstractStock market has received widespread attention from investors. It has always been a hot spot for investors and investment companies to grasp the change regularity of the stock market and predict its trend. Currently, there are many methods for stock price prediction. The prediction methods can be roughly divided into two categories: statistical methods and artificial intelligence methods. Statistical methods include logistic regression model, ARCH model, etc. Artificial intelligence methods include multi-layer perceptron, convolutional neural network, naive Bayes network, back propagation network, single-layer LSTM, support vector machine, recurrent neural network, etc. But these studies predict only one single value. In order to predict multiple values in one model, it need to design a model which can handle multiple inputs and produces multiple associated output values at the same time. For this purpose, it is proposed an associated deep recurrent neural network model with multiple inputs and multiple outputs based on long short-term memory network. The associated network model can predict the opening price, the lowest price and the highest price of a stock simultaneously. The associated network model was compared with LSTM network model and deep recurrent neural network model. The experiments show that the accuracy of the associated model is superior to the other two models in predicting multiple values at the same time, and its prediction accuracy is over 95%.

scholarly journals Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network

2021 ◽  
Vol 11 (9) ◽  
pp. 3984
Author(s):  
Xinpeng Yu ◽  
Dagang Li
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Performance Prediction ◽  
Stock Prices ◽  
Stock Price ◽  
Short Term Memory ◽  
Stock Performance ◽  
Trading Systems ◽  
Stock Price Prediction ◽  
Stock Traders

Stock performance prediction plays an important role in determining the appropriate timing of buying or selling a stock in the development of a trading system. However, precise stock price prediction is challenging because of the complexity of the internal structure of the stock price system and the diversity of external factors. Although research on forecasting stock prices has been conducted continuously, there are few examples of the successful use of stock price forecasting models to develop effective trading systems. Inspired by the process of human stock traders looking for trading opportunities, we propose a deep learning framework based on a hybrid convolutional recurrent neural network (HCRNN) to predict the important trading points (IPs) that are more likely to be followed by a significant stock price rise to capture potential high-margin opportunities. In the HCRNN model, the convolutional neural network (CNN) performs convolution on the most recent region to capture local fluctuation features, and the long short-term memory (LSTM) approach learns the long-term temporal dependencies to improve stock performance prediction. Comprehensive experiments on real stock market data prove the effectiveness of our proposed framework. Our proposed method ITPP-HCRNN achieves an annualized return that is 278.46% more than that of the market.

scholarly journals Penerapan Algoritma Levenberg-Marquadt dan Backpropagation Neural Network Untuk Klasifikasi Suara Manusia

2012 ◽  
Vol 4 (1) ◽  
Author(s):  
David David
Keyword(s):  
Neural Network ◽  
Speech Processing ◽  
Backpropagation Neural Network ◽  
Zero Crossing ◽  
Spectral Centroid ◽  
Human Voice ◽  
Voice Signal ◽  
Zero Crossing Rate ◽  
Short Time ◽  
Short Time Energy

Abstract. Voice recognition technology is currently experiencing growth, especially in the case of speech processing. Speech processing is a way to extract the desired information from a voice signal. This study discusses the classification of human voice system male and female. Extract the characteristics of the voice signal in each frame time domain and frequency domain is to help simplify and speed calculations. The features for voice or other audio between Short Time Energy, Zero Crossing Rate, Spectral Centroid, and others. Test results show that the classification system the human voice using the backpropagation neural network and Levenberg-Marquadt algorithm to change matrix weight is very good because of the complexity and rapid calculation which is not too high. Database voice sample of 40 voices with the test data as much as 5 votes. The output of the system is the result of the classification that has been identified with a similarity value>=0.5 for male and <0.5 as a female. Testing using artificial neural network produced an average success rate in voice classification amounted to 91%.Keywords: Feature Extraction, Classification, Backpropagation, Levenberg-Marquadt Algorithm, Human Voice Abstrak. Teknologi pengenalan suara saat ini telah mengalami perkembangan terutama dalam hal speech processing. Speech processing merupakan suatu cara untuk mengekstrak informasi yang diinginkan dari sebuah sinyal suara. Penelitian ini membahas sistem klasifikasi suara manusia male dan female. Mengekstrak ciri dari sinyal suara setiap frame pada kawasan waktu dan kawasan frekuensi sangat membantu untuk  menyederhanakan dan mempercepat perhitungan. Adapun fitur-fitur untuk suara atau audio antara lain Short Time Energy, Zero Crossing Rate, Spectral Centroid dan lain-lain. Hasil pengujian sistem menunjukkan bahwa klasifikasi suara manusia dengan menggunakan jaringan saraf tiruan backpropagation dan algoritma Levenberg-Marquadt untuk perubahan matriks bobot, sangat baik dan cepat karena kompleksitas perhitungan yang tidak terlalu tinggi. Database sample suara sebanyak 40 buah dengan data test sebanyak 5 suara. Output dari sistem adalah hasil klasifikasi yang telah dikenali dengan nilai kemiripan >= 0,5 sebagai pria dan < 0,5 sebagai wanita. Pengujian dengan menggunakan jaringan saraf tiruan dihasilkan rata-rata tingkat keberhasilan dalam klasifikasi suara adalah sebesar 91 %.Kata Kunci: Feature Extraction, Klasifikasi, Backpropagation, Algoritma Levenberg-Marquadt, Suara Manusia

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