Forex predicton with neural network: usd/eur currency pair
In this paper we investigate and design the neural networks model for FOREX prediction based on the historical data movement of USD/EUR exchange rates. Given that the model analyzes historical data of exchange rates, neural networks can be used as an important technique of technical analysis. Unlike many other techniques of technical analysis which are based on price trends analysis, neural networks offer autocorrelation analysis and the estimation of possible errors in forecasting. This theory is consistent with the semi-strong form of the efficient markets hypothesis. The empirical data used in the model of neural networks are related to the exchange rate USD / EUR in the period 23.04.2012 - 04.05.2012. The results shows that the model can be used for FOREX prediction.
The sixteen-volume set comprising the LNCS volumes 11205-11220 constitutes the refereed proceedings of the 15th European Conference on Computer Vision, ECCV 2018, held in Munich, Germany, in September 2018.
The 776 revised papers presented were carefully reviewed and selected from 2439 submissions. The papers are organized in topical sections on learning for vision; computational photography; human analysis; human sensing; stereo and reconstruction; optimization; matching and recognition; video attention; and poster sessions.
The article discusses development of the segmented characters classifier of the Russian alphabet a nd of the Arabic numerals on the basis of block neural network structures including the plurality of blocks for each individual character recognition and for the synthesis block decision. Keywords: pattern recognition, neural network, training of neural n etworks, base of hand - written characters, recognition of hand - written characters
The application of neural networks for prediction of long-term changes of observed parameter on the example of thermal treatment control of concrete products is considered. Experimental results are presented, and the algorithm of the work plan of an actuating mechanism is proposed.
Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users’ moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach, which allow us to evaluate presence of eight basic emotions in more than 755 million tweets. The application of Support Vectors Machine and Neural Networks algorithms to predict DJIA and S&P500 indicators are discussed.
This book constitutes revised selected papers from the First International Workshop on Machine Learning, Optimization, and Big Data, MOD 2015, held in Taormina, Sicily, Italy, in July 2015. The 32 papers presented in this volume were carefully reviewed and selected from 73 submissions. They deal with the algorithms, methods and theories relevant in data science, optimization and machine learning.
In work the developed model of adaptive management by the vertically integrated companies based on the system approach supporting the mechanism of an operational management in a uniform cycle of strategic planning, within the limits of faster time is presented. Thus for a finding of optimum values of operating parameters special algorithms of a class of genetic algorithms are used, neural networks the example of the developed system of adaptive management for the vertically-integrated oil company is etc. presented.
This book constitutes the refereed proceedings of the 6th IAPR TC3 International Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2014, held in Montreal, QC, Canada, in October 2014. The 24 revised full papers presented were carefully reviewed and selected from 37 submissions for inclusion in this volume. They cover a large range of topics in the field of learning algorithms and architectures and discussing the latest research, results, and ideas in these areas.
The paper theorizes on the general architectonics of idealized cognitive models (ICMs) and their involvement in metonymy and metaphor. The article posits that an ICM's structure should reflect the architecture of the neural network/s engaged in processing of a given concept. The ICM nodes, or cogs, construct a complex, hierarchically organized neural connections, with the superior nodes being highly selective, invariant and prototypical. Signals travelling from one cog to another within one ICM are essentially metonymical, while a cog shared by two or more ICMs marks a metaphoric shift.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.