Forecasting Market Prices with Causal-Retro-Causal Neural Networks

2012 ◽  
pp. 579-584 ◽  
Author(s):  
Hans-Georg Zimmermann ◽  
Ralph Grothmann ◽  
Christoph Tietz
Keyword(s):  
Neural Networks ◽  
Market Prices

scholarly journals Hybrid fuzzy neural network versus backpropagation neural network: An application to predict the Ibex-35 index stock

2020 ◽  
Vol 6 (1) ◽  
pp. 85-98
Author(s):  
J. Oliver Muncharaz
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Neuronal Network ◽  
Fuzzy Neural Network ◽  
Stock Exchange ◽  
Prediction Errors ◽  
Market Prices ◽  
Index Stock ◽  
Fuzzy Neural ◽  
Spanish Stock Exchange

The use of neural networks has been extended in all areas of knowledge due to the good results being obtained in the resolution of the different problems posed. The prediction of prices in general, and stock market prices in particular, represents one of the main objectives of the use of neural networks in finance. This paper presents the analysis of the efficiency of the hybrid fuzzy neural network against a backpropagation type neural network in the price prediction of the Spanish stock exchange index (IBEX-35). The paper is divided into two parts. In the first part, the main characteristics of neural networks such as hybrid fuzzy and backpropagation, their structures and learning rules are presented. In the second part, the prediction of the IBEX-35 stock exchange index with these networks is analyzed, measuring the efficiency of both as a function of the prediction errors committed. For this purpose, both networks have been constructed with the same inputs and for the same sample period. The results obtained suggest that the Hybrid fuzzy neuronal network is much more efficient than the widespread backpropagation neuronal network for the sample analysed.

Effective model of the market prices forecasting using neural networks

2019 ◽  
Author(s):  
A. A. Panachev ◽  
E. I. Komotskiy ◽  
D. B. Berg ◽  
M. A. Saif ◽  
T. B. Atanasova
Keyword(s):  
Neural Networks ◽  
Effective Model ◽  
Market Prices

Decision Support System for Enhancing Crop Productivity

2020 ◽  
Vol 17 (9) ◽  
pp. 4438-4441
Author(s):  
Meeradevi ◽  
Monica R. Mundada ◽  
Hrishikesh Salpekar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Support ◽  
Neural Network Model ◽  
Arima Model ◽  
Market Price ◽  
Crop Productivity ◽  
Market Prices ◽  
The People ◽  
The Neural Network

Agriculture is the important aspect for the people of India. The life of large percentage of people in India is dependent on agriculture. The farmers are facing difficulty in selling their product to the markets due to lack of knowledge on crop prices. The market prices changes drastically in time. Using neural networks market price can be predicted and made available to the farmers to decide the time to sell their product. The ARIMA model is used to forecast the prices which can help the farmers to improve their economy and also the crop yield is predicted using neural network in the proposed system. So, that the user can check the yield of the crop in the particular piece of land before sowing. The prediction using the neural network model results in deciding the time to sell the prices and what will be the production of the crop over the year.

The Application of Wavelet Neural Network in Prediction of the Fish Price

2014 ◽  
Vol 687-691 ◽  
pp. 1945-1949
Author(s):  
Hong Wei Li ◽  
Xiao Xiang Gao ◽  
Ke Jun Cheng
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Success Rate ◽  
Bp Algorithm ◽  
Price Forecasting ◽  
Bp Network ◽  
Market Prices ◽  
Short Period ◽  
Artificial Intelligence Technology

The market fish price is an important factor that affects the income of fishermen, so how to accurately analyze and predict the fish pricet o obtain huge profits has caught people's attention. As science advances, various price forecasting and analysis methods have come into being. How to build a prediction theories and models with relatively high success rate has been the study of many scholars over the years. With the development of artificial intelligence, neural networks have become an important tool of predicting and analyzing changes in market prices. Neural networks are important artificial intelligence technology, which have simple structures, but are able to solve complicated problems. They have strong applicability in predicting the mature index fluctuations in a short period. This paper considers some shortcomings and deficiencies the BP network prototype, which tries to use the wavelet Functions to replace the excitation function in the traditional BP algorithm on the basis of a network of neurons and then forms into WNN. We can verify the feasibility of WNN by perch price forecasts, and then this method is used in price forecasts of the three main fish of the Ulungur Lake Aquatic, to provide the basis for the aquatic base decision

scholarly journals Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1906 ◽  
Author(s):  
Christian Giovanelli ◽  
Seppo Sierla ◽  
Ryutaro Ichise ◽  
Valeriy Vyatkin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Energy Market ◽  
Market Prices ◽  
Artificial Neural

The increase of distributed energy resources in the smart grid calls for new ways to profitably exploit these resources, which can participate in day-ahead ancillary energy markets by providing flexibility. Higher profits are available for resource owners that are able to anticipate price peaks and hours of low prices or zero prices, as well as to control the resource in such a way that exploits the price fluctuations. Thus, this study presents a solution in which artificial neural networks are exploited to predict the day-ahead ancillary energy market prices. The study employs the frequency containment reserve for the normal operations market as a case study and presents the methodology utilized for the prediction of the case study ancillary market prices. The relevant data sources for predicting the market prices are identified, then the frequency containment reserve market prices are analyzed and compared with the spot market prices. In addition, the methodology describes the choices behind the definition of the model validation method and the performance evaluation coefficient utilized in the study. Moreover, the empirical processes for designing an artificial neural network model are presented. The performance of the artificial neural network model is evaluated in detail by means of several experiments, showing robustness and adaptiveness to the fast-changing price behaviors. Finally, the developed artificial neural network model is shown to have better performance than two state of the art models, support vector regression and ARIMA, respectively.

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