DeepForecasting | Stock Market Decision Support System using Deep | Predictive Analytics library
kandi X-RAY | DeepForecasting Summary
kandi X-RAY | DeepForecasting Summary
DeepForecasting is an intelligent decision support system used to predict the future company's stock price considering its stock historical data and news effect. To assist the nonfinancial expert users, we create a composite index for each company based on the best result returned from three different types of deep learning neural networks (Backpropagation, Radial Basis Function, and Recurrent) and the result obtained from the sentiment analysis of company's related news. This composite index is scored up to three, and higher values indicate more safety decisions. DeepForecasting-Plus: Displayed the stock market prediction summaries in a friendly web user interface including visualized plots and informative reports. [Screenshot].
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Top functions reviewed by kandi - BETA
- Wrapper around saw_sift
- Sift a series t
- R Compute allreextrema
- Implementation of saw - transformation
- Determines the RNN model
- Train the model
- Evaluate the model for the given model
- Mean squared error
- Prepare the training and test set
- Initialize date fields
- Return a new date with a given delta
- Convert a string to a list of integers
- Extracts news from BeautifulSoup
- Saves trend figure
- Save the trend
- Save a matplotlib figure
- Create the csv file
- Emulate the emd
- Sift a series
- Emulate the emd algorithm
- Check if y0 is within range
- Export the results to JSON
- Returns the result as a dictionary
- Make the moving average plot
- Creates a subplot
- Calculate moving average
DeepForecasting Key Features
DeepForecasting Examples and Code Snippets
Community Discussions
Trending Discussions on Predictive Analytics
QUESTION
GPU is good for parallel computing but the problem is some machine learning libraries don't utilize the GPU, unless that machine learning based on image processing or some sort of graphics processing, what if I am using machine learning for predictive Analytics? do libraries like TensorFlow utilize the GPU? or they use only CPU? or can I choose which processing unit to use? whats the deal here?
note: predictive Analysis requires no graphics processing.
...ANSWER
Answered 2020-Nov-21 at 21:35The computation that happens in the GPU in any of the machine learning frameworks that support GPUs is not limited to graphical processing. For instance, if your model is a simple logistic regression, a framework such as TensorFlow will run it on the GPU if properly configured.
The advantage of GPUs for machine learning is that training big neural networks benefits greatly from the high level of parallelism that the GPUs offer.
If you want to know more about this, I'd recommend you start here or here.
some things to consider:- how much a model will benefit from running in the GPU will depend on how much it will benefit from parallel computation in general.
- Deep Learning models can be applied to predictive analytics, as well as more classical machine learning models. Bear in mind that neural nets are possibly the category of models that will benefit inherently from the GPU (see links above).
- Even though running models using GPUs (or even more specialised hardware) can bring benefits, I would suggest that you don't choose a framework and, especially, don't choose an algorithm based solely on the fact that it will benefit from parallelism, but rather look at how appropriate a given algorithm is for the data you have.
QUESTION
I have a pandas dataframe which is a large number of answers given by users in response to a survey and I need to re-structure it. There are up to 105 questions asked each year, but I only need maybe 20 of them.
The current structure is as below.
What I want to do is re-structure it so that the row values become column names and the answer given by the user is then the value in that column. In a picture (from Excel), what I want is the below (I know I'll need to re-name my columns, but that's fine once I can create the structure in the first place):
Is it possible to re-structure my dataframe this way? The outcome of this is to use some predictive analytics to predict a target variable, so I need to re-strcture before I can use Random Forest, kNN, and so on.
...ANSWER
Answered 2020-Nov-01 at 19:39You might want try pivoting your table:
QUESTION
I have js files Dashboard and Adverts. I managed to get Dashboard to list the information in one json file (advertisers), but when clicking on an advertiser I want it to navigate to a separate page that will display some data (Say title and text) from the second json file (productadverts). I can't get it to work. Below is the code for the Dashboard and next for Adverts. Then the json files
...ANSWER
Answered 2020-May-17 at 23:55The new object to get params in React Navigation 5 is:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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No vulnerabilities reported
Install DeepForecasting
You can use DeepForecasting like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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