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.
Mann Kendall trend test is suitable for analyzing time series data with continuous growth or downward trend (monotonic trend). It is a nonparametric test, which is applicable to all distributions (i.e. the data does not need to meet the assumption of normal distribution), but the data should have no sequence correlation. If the data have sequence correlation, it will have an impact on the significance level (P value). In order to solve this problem, the researchers proposed several improved Mann Kendall tests (Hamed and Rao improved MK test, Yue and Wang improved MK test, etc.). Seasonal Mann Kendall test can also exclude the influence of seasonality.
Installation: need python environment;
Input: time series data
Output: slope,intercept, Significance level;
Dependent library files: numpy，scipy
2019-11-10 9631 View Details
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;
2019-11-07 9547 View Details
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