Data Analysis
- jupyter-notebook
3
GitHub Stars
jupyter-notebook
Language
6 months ago
First Indexed
2 months ago
Catalog Refreshed
Documentation & install
Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.
Available tools
Correlation analysis
Compute Pearson, Spearman, or Kendall correlation coefficients between two data series to quantify linear and monotonic relationships.
Stationarity tests
Perform ADF, PP, and KPSS tests to assess whether a time series is stationary and suitable for forecasting models.
Distribution analysis
Analyze distribution characteristics and trends of a single variable to understand its behavior.
Outlier detection
Detect anomalies using multiple methods and provide an overall assessment of data quality.
Causality analysis
Conduct Granger causality tests and cross-correlation analyses to infer potential causal relationships between variables.
Time-series similarity
Measure similarity between two sequences using methods like DTW and sliding window analysis.
Forecasting
Forecast future values for minute-level data using polynomial trend and related approaches.
