Earthly Probabilistic Personal profiles for Sepsis Computing in ICS

The tasks of classification and prediction, given a set of multivariate time series, typically involve detecting an underlying situation, such as a malfunction, or predicting an outcome, such as a medical complication. However, calculating similarities between different sets of multivariate time series is challenging when dealing with problem of missing data, different sampling frequencies, and random noise in cases of high-dimensional data. Here, we propose a new dynamic-behavior-based model, which we call a Temporal Probabilistic proFile (TPF), for classification and prediction tasks of multivariate time series. In the TPF method, the raw data are first abstracted into a series of intervals characterizing meaningful periods. We then discover frequently repeating temporal patterns within the data. Using the discovered patterns, we create a probabilistic distribution of the temporal patterns of the overall entity population, of each class in it, and of each entity. We then exploit TPFs to classify the time series of new entities, or to predict their outcome, by measuring their TPF distance, either to the aggregated TPF of each class, or to the individual TPFs of each of the entities, using negative cross entropy.

Researchers: Eitam Sheetrit, Nir Nissim, Denis Klimov, Yuval Shahar
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Selecting the „best“ features for your Machine Learning model will result in a better performing, easier to understand, and faster running model. But how do you know which features to select?

In this video, I’ll discuss 7 feature selection tactics used by the pros that you can apply to your own model. At the end, I’ll give you my top 3 tips for effective feature selection.

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=== RELATED RESOURCES ===

Dimensionality reduction presentation: https://www.youtube.com/watch?v=ioXKxulmwVQ
Feature selection in scikit-learn: http://scikit-learn.org/stable/modules/feature_selection.html
Sequential Feature Selector from mlxtend: http://rasbt.github.io/mlxtend/user_guide/feature_selection/SequentialFeatureSelector/

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How Trend Forecasting Makes Trading A lot simpler

Although TradeShark uses sophisticated technology to generate price forecasts, interpreting the indicators is simple and clear for all levels of traders. TradeShark indicators forecast trends with several time parameters, show how likely a trend is to continue during the next few days and predict the next day’s high and low price. Those are all valuable tools, especially for swing traders who trade short-term positions. See more at http://www.tradeshark.com/
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How to Calculate and Trade With the TSF Indicator in Excel

The Time Series Forecast (TSF) indicator is based on the linear regression. It is used by traders to forecast the future price. In this video I show how it can be calculated using Excel. I then show how this indicator can be used the trade the GBP/USD forex pair based on historic price data.

Read the accompanying article for more information and the strategy results: http://bit.ly/1sC4rnM

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The New York Stock Exchange (sometimes referred to as „the Big Board“) provides a means for buyers and sellers to trade shares of stock in companies registered for public trading. The NYSE is open for trading Monday through Friday from 9:30 am — 4:00 pm ET, with the exception of holidays declared by the Exchange in advance.

The NYSE trades in a continuous auction format, where traders can execute stock transactions on behalf of investors. They will gather around the appropriate post where a specialist broker, who is employed by an NYSE member firm (that is, he/she is not an employee of the New York Stock Exchange), acts as an auctioneer in an open outcry auction market environment to bring buyers and sellers together and to manage the actual auction. They do on occasion (approximately 10% of the time) facilitate the trades by committing their own capital and as a matter of course disseminate information to the crowd that helps to bring buyers and sellers together. The auction process moved toward automation in 1995 through the use of wireless hand held computers (HHC). The system enabled traders to receive and execute orders electronically via wireless transmission. On September 25, 1995, NYSE member Michael Einersen, who designed and developed this system, executed 1000 shares of IBM through this HHC ending a 203 year process of paper transactions and ushering in an era of automated trading.

As of January 24, 2007, all NYSE stocks can be traded via its electronic hybrid market (except for a small group of very high-priced stocks). Customers can now send orders for immediate electronic execution, or route orders to the floor for trade in the auction market. In the first three months of 2007, in excess of 82% of all order volume was delivered to the floor electronically.[23] NYSE works with US regulators like the SEC and CFTC to coordinate risk management measures in the electronic trading environment through the implementation of mechanisms like circuit breakers and liquidity replenishment points.[24]

Until 2005, the right to directly trade shares on the exchange was conferred upon owners of the 1366 „seats“. The term comes from the fact that up until the 1870s NYSE members sat in chairs to trade. In 1868, the number of seats was fixed at 533, and this number was increased several times over the years. In 1953, the number of seats was set at 1,366. These seats were a sought-after commodity as they conferred the ability to directly trade stock on the NYSE, and seat holders were commonly referred to as members of the NYSE. The Barnes family is the only known lineage to have five generations of NYSE members: Winthrop H. Barnes (admitted 1894), Richard W.P. Barnes (admitted 1926), Richard S. Barnes (admitted 1951), Robert H. Barnes (admitted 1972), Derek J. Barnes (admitted 2003). Seat prices varied widely over the years, generally falling during recessions and rising during economic expansions. The most expensive inflation-adjusted seat was sold in 1929 for 5,000, which, today, would be over six million dollars. In recent times, seats have sold for as high as million in the late 1990s and as low as million in 2001. In 2005, seat prices shot up to .25 million as the exchange entered into an agreement to merge with Archipelago and become a for-profit, publicly traded company. Seat owners received 0,000 in cash per seat and 77,000 shares of the newly formed corporation. The NYSE now sells one-year licenses to trade directly on the exchange. Licences for floor trading are available for ,000 and a licence for bond trading is available for as little as ,000 as of 2010.[25] Neither are resell-able, but may be transferable in during the change of ownership of a cooperation holding a trading licence.

On February 15, 2011 NYSE and Deutsche Börse announced their merger to form a new company, as yet unnamed, wherein Deutsche Börse shareholders will have 60% ownership of the new entity, and NYSE Euronext shareholders will have 40%.

On February 1, 2012, the European Commission blocked the merger of NYSE with Deutsche Börse, after commissioner Joaquin Almunia stated that the merger „would have led to a near-monopoly in European financial derivatives worldwide“.[38] Instead, Deutsche Börse and NYSE will have to sell either their Eurex derivatives or LIFFE shares in order to not create a monopoly. On February 2, 2012, NYSE Euronext and Deutsche Börse agreed to scrap the merger.[39]

In April 2011, IntercontinentalExchange (ICE), an American futures exchange, and NASDAQ OMX Group had together made an unsolicited proposal to buy NYSE Euronext for approximately US billion, a deal in which NASDAQ would have taken control of the stock exchanges.[40] NYSE Euronext rejected this offer two times, but it was finally terminated after the United States Department of Justice indicated their intention to block the deal due to antitrust concerns.

http://en.wikipedia.org/wiki/New_York_Stock_Exchange
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Stock Market Prophecy

Can we predict the price of Microsoft stock using Machine Learning? We’ll train the Random Forest, Linear Regression, and Perceptron models on many years of historical price data as well as sentiment from news headlines to find out!

Code for this video:
https://github.com/llSourcell/Stock_Market_Prediction

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More learning resources:
https://www.quantinsti.com/blog/machine-learning-trading-predict-stock-prices-regression/
https://medium.com/@TalPerry/deep-learning-the-stock-market-df853d139e02
https://iknowfirst.com/rsar-machine-learning-trading-stock-market-and-chaos
https://www.udacity.com/course/machine-learning-for-trading–ud501
https://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets
https://quant.stackexchange.com/questions/111/how-can-i-go-about-applying-machine-learning-algorithms-to-stock-markets
http://eugenezhulenev.com/blog/2014/11/14/stock-price-prediction-with-big-data-and-machine-learning/
https://cloud.google.com/solutions/machine-learning-with-financial-time-series-data
https://www.linkedin.com/pulse/deep-learning-stock-price-prediction-explained-joe-ellsworth

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-How do we become global investors with globally-connected markets?
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-Traders using an Artificial Intelligence trading tool have the edge. AI has the ability to process mass amounts of data and overcome emotional struggles and human capabilities

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