exp-smoothing-java | Exponential Smoothing & Moving Average Models | Predictive Analytics library

 by   navdeep-G Java Version: Current License: No License

kandi X-RAY | exp-smoothing-java Summary

kandi X-RAY | exp-smoothing-java Summary

exp-smoothing-java is a Java library typically used in Analytics, Predictive Analytics applications. exp-smoothing-java has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

Exponential Smoothing & Moving Average Models in Java & H2O-3
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            kandi-support Support

              exp-smoothing-java has a low active ecosystem.
              It has 17 star(s) with 7 fork(s). There are 5 watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              There are 1 open issues and 0 have been closed. On average issues are closed in 527 days. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of exp-smoothing-java is current.

            kandi-Quality Quality

              exp-smoothing-java has 0 bugs and 0 code smells.

            kandi-Security Security

              exp-smoothing-java has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              exp-smoothing-java code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              exp-smoothing-java does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              exp-smoothing-java releases are not available. You will need to build from source code and install.
              Build file is available. You can build the component from source.
              exp-smoothing-java saves you 838 person hours of effort in developing the same functionality from scratch.
              It has 1921 lines of code, 180 functions and 35 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed exp-smoothing-java and discovered the below as its top functions. This is intended to give you an instant insight into exp-smoothing-java implemented functionality, and help decide if they suit your requirements.
            • Generates the forecast for a time series
            • Calculates the list of seasonal indices
            • This method is used to calculate the backbone variables
            • Calculates the forecast
            • Convert a list to a list
            • Adds a value to the average
            • Gets the average
            • Adds a new number
            • Calculates the ema value
            • Update the average value
            • Returns the average of the time series
            • Read in a series of time series
            • Returns the maximum value based on the index
            • Creates exponential forecast for a list of data
            • Returns the auto - covariance
            • Returns the minimum value
            • Returns the variance
            • Returns the sqrt vector
            • Creates a single exponential forecast for the given data
            • Cbrt
            • Get the minimum value index
            • Calculates root of a data
            • Calculates log of a list
            • Returns the maximum value index
            • Read a series of timeseries from a file
            • Returns the standard deviation
            Get all kandi verified functions for this library.

            exp-smoothing-java Key Features

            No Key Features are available at this moment for exp-smoothing-java.

            exp-smoothing-java Examples and Code Snippets

            No Code Snippets are available at this moment for exp-smoothing-java.

            Community Discussions

            QUESTION

            will TensorFlow utilize GPU for predictive Analysis?
            Asked 2020-Nov-21 at 21:35

            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 21:35
            The short answer: yes, it will! The slightly longer answer:

            The 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.

            Source https://stackoverflow.com/questions/64948197

            QUESTION

            Restructuring Pandas Dataframe for large number of columns
            Asked 2020-Nov-01 at 19:39

            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 19:39

            You might want try pivoting your table:

            Source https://stackoverflow.com/questions/64630691

            QUESTION

            Display data from two json files in react native
            Asked 2020-May-17 at 23:55

            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 23:55

            The new object to get params in React Navigation 5 is:

            Source https://stackoverflow.com/questions/61859411

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install exp-smoothing-java

            You can download it from GitHub.
            You can use exp-smoothing-java like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the exp-smoothing-java component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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