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<description>henrik&#39;s bookmarks tagged &quot;stock&quot; on Netvouz</description>
<item><title>LSTM in Python: Stock Market Predictions (article) - DataCamp</title>
<link>https://www.datacamp.com/community/tutorials/lstm-python-stock-market</link>
<description>Discover Long Short-Term Memory (LSTM) networks in Python and how you can use them to make stock market predictions! In this tutorial, you will see how you can use a time-series model known as Long Short-Term Memory. LSTM models are powerful, especially for retaining a long-term memory, by design, as you will see later.</description>
<category domain="http://www.netvouz.com/henrik?category=968349102261480250">Artificial Intelligence AI</category>
<author>henrik</author>
<pubDate>Tue, 23 Jul 2019 14:50:24 GMT</pubDate>
</item><item><title>FreeRealTime</title>
<link>http://www.freerealtime.com</link>
<description>US stock quotes</description>
<category domain="http://www.netvouz.com/henrik?category=1553807154759995371">Ekonomi &gt; Aktier &gt; Utländska</category>
<author>henrik</author>
<pubDate>Fri, 07 Apr 2000 22:00:00 GMT</pubDate>
</item><item><title>Predicting Stock Price with LSTM - Towards Data Science</title>
<link>https://towardsdatascience.com/predicting-stock-price-with-lstm-13af86a74944</link>
<description>Machine learning has found its applications in many interesting fields over these years. Taming stock market is one of them. I had been thinking of giving it a shot for quite some time now; mostly to solidify my working knowledge of LSTMs. And finally I have finished the project and quite excited to share my experience.</description>
<category domain="http://www.netvouz.com/henrik?category=968349102261480250">Artificial Intelligence AI</category>
<author>henrik</author>
<pubDate>Mon, 22 Jul 2019 10:57:40 GMT</pubDate>
</item><item><title>Stock Market Prediction by Recurrent Neural Network on LSTM Model</title>
<link>https://blog.usejournal.com/stock-market-prediction-by-recurrent-neural-network-on-lstm-model-56de700bff68</link>
<description>The art of forecasting stock prices has been a difficult task for many of the researchers and analysts. In fact, investors are highly interested in the research area of stock price prediction. For a good and successful investment, many investors are keen on knowing the future situation of the stock market. Good and effective prediction systems for stock market help traders, investors, and analyst by providing supportive information like the future direction of the stock market. In this work, we present a recurrent neural network (RNN) and Long Short-Term Memory (LSTM) approach to predict stock market indices.</description>
<category domain="http://www.netvouz.com/henrik?category=968349102261480250">Artificial Intelligence AI</category>
<author>henrik</author>
<pubDate>Mon, 22 Jul 2019 10:53:46 GMT</pubDate>
</item><item><title>Time Series Prediction Using LSTM Deep Neural Networks</title>
<link>https://www.altumintelligence.com/articles/a/Time-Series-Prediction-Using-LSTM-Deep-Neural-Networks</link>
<description>This article focuses on using a Deep LSTM Neural Network architecture to provide multidimensional time series forecasting using Keras and Tensorflow - specifically on stock market datasets to provide momentum indicators of stock price. The code for this framework can be found in the following GitHub repo (it assumes python version 3.5.x and the requirement versions in the requirements.txt file. Deviating from these versions might cause errors): https://github.com/jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction The following article sections will briefly touch on LSTM neuron cells, give a toy example of predicting a sine wave then walk through the application to a stochastic time series. The article assumes a basic working knowledge of simple deep n</description>
<category domain="http://www.netvouz.com/henrik?category=968349102261480250">Artificial Intelligence AI</category>
<author>henrik</author>
<pubDate>Tue, 23 Jul 2019 14:25:44 GMT</pubDate>
</item><item><title>WorldWide Financial Network</title>
<link>http://wwfn.com</link>
<description>Portal; with US Stock Market Crash Index</description>
<category domain="http://www.netvouz.com/henrik?category=1553807154759995371">Ekonomi &gt; Aktier &gt; Utländska</category>
<author>henrik</author>
<pubDate>Sat, 22 Apr 2000 22:00:00 GMT</pubDate>
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