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  • LSTM in Python: Stock Market Predictions (article) - DataCamp
    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.
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  • How to Develop LSTM Models for Time Series Forecasting
    Long Short-Term Memory networks, or LSTMs for short, can be applied to time series forecasting. There are many types of LSTM models that can be used for each specific type of time series forecasting problem. In this tutorial, you will discover how to develop a suite of LSTM models for a range of standard time series forecasting problems.
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  • Multivariate Time Series Forecasting with LSTMs in Keras
    Neural networks like Long Short-Term Memory (LSTM) recurrent neural networks are able to almost seamlessly model problems with multiple input variables. This is a great benefit in time series forecasting, where classical linear methods can be difficult to adapt to multivariate or multiple input forecasting problems. In this tutorial, you will discover how you can develop an LSTM model for multivariate time series forecasting in the Keras deep learning library. After completing this tutorial, you will know: How to transform a raw dataset into something we can use for time series forecasting. How to prepare data and fit an LSTM for a multivariate time series forecasting problem. How to make a forecast and rescale the result back into the original units.
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  • RNN Training Tips and Tricks
    Monitoring Validation Loss vs. Training Loss If you’re somewhat new to Machine Learning or Neural Networks it can take a bit of expertise to get good models. The most important quantity to keep track of is the difference between your training loss (printed during training) and the validation loss (printed once in a while when the RNN is run on the validation data (by default every 1000 iterations)). In particular: If your training loss is much lower than validation loss then this means the network might be overfitting. Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on.
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  • Stock Market Prediction by Recurrent Neural Network on LSTM Model
    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.
    in Artificial Intelligence & Cognitive computing with artificial github intelligence learning lstm machine market prediction rnn stock
  • Time Series Prediction Using LSTM Deep Neural Networks
    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): 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
    in Artificial Intelligence & Cognitive computing with forecasting keras lstm network neural price stock tensorflow

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