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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.

  • Breaks for Additive Seasonal and Trend

    BFAST integrates the decomposition of time series into trend, seasonal, and remainder components with methods for detecting and characterizing abrupt changes within the trend and seasonal components. BFAST can be used to analyze different types of satellite image time series and can be applied to other disciplines dealing with seasonal or non-seasonal time series, such as hydrology, climatology, and econometrics. The algorithm can be extended to label detected changes with information on the parameters of the fitted piecewise linear models. BFAST monitor provides functionality to detect disturbance in near real-time based on BFAST-type models.

    Installation: need R environment;

    Input: time series data

    Output: the fited trend component,the fited seasonal component, the noise or remainder component;

    QR code:

    2019-10-14 914 View Details

  • Mann-Kendall Trend Test

    The Mann-Kendall Trend Test is used to analyze data collected over time for consistently increasing or decreasing trends (monotonic) in Y values. It is a non-parametric test, which means it works for all distributions (i.e. your data doesn’t have to meet the assumption of normality), but your data should have no serial correlation. This package also include some revised version of Mann-Kendall method.

    Installation: need python environment;

    Input: time series data

    Output: trend, intercept, significance;

    Depends: numpy,scipy

    QR code:

    2019-10-14 1152 View Details

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