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.
Starting from the causal mechanism of data generation, the causal function model is used to determine the causal relationship and identify the causal direction.
Installation: python
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Granger causality analysis method, the inference standard is the prediction error of the regression model.
Installation: python
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Based on Takens' theorem, CCM infers causality through state space reconstruction.
Installation: matlab
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Input variable: time series data
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Causal discovery means finding causal relationship from observed data, but in many cases, the causal relationship cannot be simply determined according to the sequence of events. For example, the decline of the mercury column of the barometer is related to the increase of the probability of rain, and the decline of the mercury column is earlier than the rain, but the fact is that there is no causal relationship between the two. The correlation between them is that the decline of the mercury column and the rain are caused by the decrease of the atmospheric pressure at the same time.
Installation: matlab
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