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NW.UnivariateForecasting is a library to perform Univariate Forecasting on your own values. Time Series Forecasting is a machine learning technique that aims to predict the next values in a time series when a subset of subsequent values is provided ("sliding window").

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numbworks/NW.UnivariateForecasting

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NW.UnivariateForecasting

Library Client
codecoverage_library.svg codecoverage_client.svg

Contact: [email protected]

Revision History

Date Author Description
2020-12-06 numbworks Created.
2020-12-22 numbworks Added Revision History.
2020-12-26 numbworks Added Download* paragraphs.
2020-12-27 numbworks Added code coverage badge.
2021-10-11 numbworks Version numbers removed.
2023-01-22 numbworks Updated to v2.5.0.
2024-02-08 numbworks Updated to v4.0.0.

In short

From the documentation:

NW.UnivariateForecasting is a library to perform univariate forecasting tasks on the values you provide.

NW.UnivariateForecastingClient (unifor.exe) is the command-line client for the NW.UnivariateForecasting library.

Download the source code

I assume you are on Windows, but the library should compile without issues on Linux as well. Please:

  1. Install Git for Windows;
  2. Open Windows Powershell (or Windows Terminal or similar) and type:
PS C:\> mkdir NW.UnivariateForecasting
PS C:\> cd .\NW.UnivariateForecasting\
PS C:\NW.UnivariateForecasting> git clone https://github.com/numbworks/NW.UnivariateForecasting.git
  1. Open NW.UnivariateForecasting.sln with Visual Studio or other IDE;
  2. Done!

Download the library's binary package

If you are a .NET developer and you want to use the library from within your projects, the binary packages are available on NuGet.

Download the client's binary package

If you are an non-developer user, you can download the client from the Releases page on Github.

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NW.UnivariateForecasting is a library to perform Univariate Forecasting on your own values. Time Series Forecasting is a machine learning technique that aims to predict the next values in a time series when a subset of subsequent values is provided ("sliding window").

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