finbrain.tech Report : Visit Site


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    The main IP address: 198.54.115.137,Your server United States,Austin ISP:Ross Technology Inc.  TLD:tech CountryCode:US

    The description :home about services pricing finbrain technologies ai-enabled financial prediction technologies register/login blog sign up log in sign up for free and start your free trial today! email address * set...

    This report updates in 29-Sep-2018

Technical data of the finbrain.tech


Geo IP provides you such as latitude, longitude and ISP (Internet Service Provider) etc. informations. Our GeoIP service found where is host finbrain.tech. Currently, hosted in United States and its service provider is Ross Technology Inc. .

Latitude: 30.2642993927
Longitude: -97.865341186523
Country: United States (US)
City: Austin
Region: Texas
ISP: Ross Technology Inc.

HTTP Header Analysis


HTTP Header information is a part of HTTP protocol that a user's browser sends to called Apache containing the details of what the browser wants and will accept back from the web server.

Content-Length:38986
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Server:Apache
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Date:Fri, 28 Sep 2018 22:54:45 GMT
Content-Type:text/html; charset=UTF-8
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DNS

soa:dns1.namecheaphosting.com. audit.namecheaphosting.com. 2018021203 86400 7200 3600000 86400
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dns2.namecheaphosting.com.
ipv4:IP:198.54.115.137
ASN:22612
OWNER:NAMECHEAP-NET - Namecheap, Inc., US
Country:US
mx:MX preference = 0, mail exchanger = mail.finbrain.tech.

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home about services pricing finbrain technologies ai-enabled financial prediction technologies register/login blog sign up log in sign up for free and start your free trial today! email address * set a password * select your country afghanistan åland islands albania algeria american samoa andorra angola anguilla antarctica antigua and barbuda argentina armenia aruba australia austria azerbaijan bahamas bahrain bangladesh barbados belarus belgium belize benin bermuda bhutan bolivia, plurinational state of bonaire, sint eustatius and saba bosnia and herzegovina botswana bouvet island brazil british indian ocean territory brunei darussalam bulgaria burkina faso burundi cambodia cameroon canada cape verde cayman islands central african republic chad chile china christmas island cocos (keeling) islands colombia comoros congo congo, the democratic republic of the cook islands costa rica côte d'ivoire croatia cuba curaçao cyprus czech republic denmark djibouti dominica dominican republic ecuador egypt el salvador equatorial guinea eritrea estonia ethiopia falkland islands (malvinas) faroe islands fiji finland france french guiana french polynesia french southern territories gabon gambia georgia germany ghana gibraltar greece greenland grenada guadeloupe guam guatemala guernsey guinea guinea-bissau guyana haiti heard island and mcdonald islands holy see (vatican city state) honduras hong kong hungary iceland india indonesia iran, islamic republic of iraq ireland isle of man israel italy jamaica japan jersey jordan kazakhstan kenya kiribati korea, democratic people's republic of korea, republic of kuwait kyrgyzstan lao people's democratic republic latvia lebanon lesotho liberia libya liechtenstein lithuania luxembourg macao macedonia, the former yugoslav republic of madagascar malawi malaysia maldives mali malta marshall islands martinique mauritania mauritius mayotte mexico micronesia, federated states of moldova, republic of monaco mongolia montenegro montserrat morocco mozambique myanmar namibia nauru nepal netherlands new caledonia new zealand nicaragua niger nigeria niue norfolk island northern mariana islands norway oman pakistan palau palestinian territory, occupied panama papua new guinea paraguay peru philippines pitcairn poland portugal puerto rico qatar réunion romania russian federation rwanda saint barthélemy saint helena, ascension and tristan da cunha saint kitts and nevis saint lucia saint martin (french part) saint pierre and miquelon saint vincent and the grenadines samoa san marino sao tome and principe saudi arabia senegal serbia seychelles sierra leone singapore sint maarten (dutch part) slovakia slovenia solomon islands somalia south africa south georgia and the south sandwich islands south sudan spain sri lanka sudan suriname svalbard and jan mayen swaziland sweden switzerland syrian arab republic taiwan, province of china tajikistan tanzania, united republic of thailand timor-leste togo tokelau tonga trinidad and tobago tunisia turkey turkmenistan turks and caicos islands tuvalu uganda ukraine united arab emirates united kingdom united states united states minor outlying islands uruguay uzbekistan vanuatu venezuela, bolivarian republic of viet nam virgin islands, british virgin islands, u.s. wallis and futuna western sahara yemen zambia zimbabwe i've accepted the terms of use. and privacy policy start your free trial welcome back! email address * password * forgot password? log in cutting edge deep learning technologies applied to financial prediction artificial neural networks use hardware and software to approximate the learning behavior of the neurons in the human brain. anns have been very successful in figuring out how humans learn concepts. analyzing time series data historical market data is the time series data from the past. it is one of the most important and the most valuable components for speculating about future prices. processing massive amounts of information with more data, hence more information is available, it is possible to make better conclusions about what will happen in the next time period - assuming that the data consists of a good enough feature to generalize well. deep learning beyond human capacity ann/deep learning algorithms can "learn" the complex relations between the time series data by analyzing vast amounts of market data, which is way beyond the human capacity to detect and interpret, and involves no human emotional bias. neural networks for complex deep learning tasks with sufficient data, we can train the anns to learn the best relationship between inputs and outputs, and then it can be set to perform such a task repeatedly. self-learning and self-adapting algorithms for all financial instruments a deep neural network is at the center of deep learning. and training the neural network actually means adjusting the weights between the pairs of neurons by minimizing the loss function. brand new approach to analyze non-linear financial data applying deep learning methods to financial problems can produce more useful results than standard methods such as fundamental analysis and technical analysis. time series data is non-linear and can only be analyzed by non- linear methods, and that is where anns come into play. how do finbrain's deep learning algorithms predict the future price movements of the financial assets? finbrain adapts deep learning technologies to financial prediction ai is transforming the financial world, early adopters have the advantage finbrain technologies in financial prediction finbrain is a fintech company, which develops deep learning algorithms to provide our customers with the most precise future predictions of stocks, commodities, foreign currencies, indexes and etfs. finbrain’s algorithm our algorithm uses sophisticated deep learning/artificial neural network approach for time series analysis. time series data is processed using self-adapting and self-optimizing algorithms, and is fed into the neural network. the network is then trained, and the multi-day ahead predictions are generated. a complex model for prediction finbrain’s algorithm is a combination of different mathematical and deep learning approaches, which analyzes the characteristics of time-series data for any stock, commodity, foreign currency, index and etf. finbrain’s advantages over the traditional methods 1 fundamental analysis and technical analysis are the financial prediction methods of the 20 th century 2 finbrain’s artificial intelligence/deep learning algorithms brings the financial prediction to the next level 3 old school techniques fall short to capture the non-linear characteristics of the financial data, whereas artificial neural networks are non-linear by their nature. a new approach for beating the traditional methods our cutting edge artificial intelligence algorithm beats the traditional fundamental analysis and technical analysis approaches which are used to predict the market instruments, and are involving large human bias. a complex model for incorporating ai and finance finbrain’s prediction algorithm incorporates complex mathematical models to process the data, determine the neural network’s hyperparameters and optimize the prediction performance of the multi-layer perceptron. great performance on capturing non-linearities the financial time series data has non-linear dynamics, and can only be analyzed with non-linear methods. thus, traditional fundamental analysis and technical analysis methods are falling short to capture the non-linear characteristics of financial market data. best fit for financial prediction the ai technology developed by finbrain extracts correlations between large amounts of multi-dimensional datasets, analyzes their non-linear time series behaviour at speeds that no human can visualize or calculate. financial instruments predicted by our algorithm stocks 505 stocks in s&p500; index, 3269 stocks in nasdaq index, 3133 stocks in nyse index commodities 30 commodi

URL analysis for finbrain.tech


https://finbrain.tech/#signup
https://finbrain.tech/#login_bottom
https://finbrain.tech/#blog
https://finbrain.tech/blog/2018/08/08/why-should-people-utilize-artificial-intelligence-in-financial-prediction/
https://finbrain.tech/blog/2018/09/26/finbrain-technologies-at-ai-in-finance-summit-new-york/
https://finbrain.tech/#services
https://finbrain.tech/#pricing
https://finbrain.tech/#about
https://finbrain.tech/blog/2018/08/24/reworks-interview-with-ahmet-salim-bilgin-founder-of-finbrain-technologies/
https://finbrain.tech/#home
https://finbrain.tech/#signup_bottom
https://finbrain.tech/#login

Whois Information


Whois is a protocol that is access to registering information. You can reach when the website was registered, when it will be expire, what is contact details of the site with the following informations. In a nutshell, it includes these informations;


SERVERS

  SERVER http://www.new.net/search_whois.tp?domain=finbrain.tech&tld=org.za

  ARGS finbrain.tech

  PORT 43

DOMAIN

NSERVER

  DNS1.NAMECHEAPHOSTING.COM 216.87.155.33

  DNS2.NAMECHEAPHOSTING.COM 216.87.152.33

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Mistakes


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