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Through incremental integration and independent research and development, build a method library of big data quality control, automatic modeling and analysis, data mining and interactive visualization, form a tool library with high reliability, high scalability, high efficiency and high fault tolerance, realize the integration and sharing of collaborative analysis methods of multi-source heterogeneous, multi-granularity, multi-phase, long-time series big data in three pole environment, as well as high Efficient and online big data analysis and processing.

  • Moving Average Model

    Moving average is a model of stationary time series.

    Installation mode: Install MATLAB;

    Operation mode: Running on MATLAB;

    Input variables: Time series data;

    Output variables: Time series predictive value;

    Dependent library files: Packed into the Dependent function folder

    QR code:



    2019-10-12 336 View Details

  • Auto-Regressive Model

    Auto-regressive is a model of stationary time series.

    Installation mode: Install MATLAB;

    Operation mode: Running on MATLAB;

    Input variables: Time series data;

    Output variables: Time series predictive value;

    Dependent library files: Packed into the Dependent function folder

    QR code:



    2019-10-12 286 View Details

  • Auto-Regressive Intergrated Moving Average Model

    Auto-regressive intergrated moving average is a model of nonstationary time series.

    Installation mode: Install MATLAB;

    Operation mode: Running on MATLAB;

    Input variables: Time series data;

    Output variables: Time series predictive value;

    Dependent library files: Packed into the Dependent function folder

    QR code:



    2019-10-12 651 View Details

  • Auto-Regressive Moving Average Model

    Auto-regressive moving average is a model of stationary time series.

    Installation mode: Install MATLAB;

    Operation mode: Running on MATLAB;

    Input variables: Time series data;

    Output variables: Time series predictive value;

    Dependent library files: Packed into the Dependent function folder

    QR code:

    2019-10-12 312 View Details

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