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专业的数据分析工具集，提供数据趋势分析、质量分析、平稳性检验、时间序列分析、相关性分析、因果关系分析等功能。
Configuration
View docs{
"mcpServers": {
"boyzhang666-data-analysis-mcp": {
"url": "http://127.0.0.1:6003/mcp"
}
}
}Compute Pearson, Spearman, or Kendall correlation coefficients between two data series to quantify linear and monotonic relationships.
Perform ADF, PP, and KPSS tests to assess whether a time series is stationary and suitable for forecasting models.
Analyze distribution characteristics and trends of a single variable to understand its behavior.
Detect anomalies using multiple methods and provide an overall assessment of data quality.
Conduct Granger causality tests and cross-correlation analyses to infer potential causal relationships between variables.
Measure similarity between two sequences using methods like DTW and sliding window analysis.
Forecast future values for minute-level data using polynomial trend and related approaches.