Claude Cookbooks

Auto-correlation analysis for time series dependency and pattern detection

Fast Fourier Transform for frequency domain analysis and spectral decomposition

K-Nearest Neighbors for classification and regression

ROC curve and AUC analysis for binary classification evaluation

Support Vector Machine for linear and non-linear classification

Time series differencing for stationarity and trend removal

Approximate nearest neighbor search using HNSW algorithm

Inverse Fast Fourier Transform for time domain reconstruction

Partial auto-correlation analysis for direct lag relationships

SHAP values for model explainability and feature importance

Analysis of variance for comparing group means

Chi-square test of independence for categorical variables

F-test for comparing variances between two groups

Path analysis for sequential event patterns

Synthetic Minority Oversampling for imbalanced datasets

Z-test for population mean comparison with known variance

Signal detrending for baseline correction and trend removal

Ljung-Box portmanteau tests for model diagnostics and residual analysis

XGBoost gradient boosting for classification and regression

Signal resampling and interpolation for rate conversion

Automated ARIMA model selection and forecasting

Power spectrum analysis for frequency domain insights and periodicity detection

Signal smoothing and noise reduction techniques

Session analysis and user journey tracking

Silhouette coefficient for clustering quality evaluation

Convolution operations for signal processing and filtering

Change point detection in time series for structural breaks

Score data using ONNX models (Bring Your Own Model)

Score data using PMML models (Bring Your Own Model)

ARIMA parameter estimation for seasonal and non-seasonal AR, MA, ARMA, and ARIMA models

ARIMA-based time series forecasting for trend and seasonal predictions

Decision forest ensemble classifier for robust predictions

Moving average based forecasting for smoothed predictions

Word and document embeddings for text analytics

Ordinal encoding for ordered categorical variables

Statistical tests for time series stationarity (ADF, KPSS, PP tests)

Seasonal pattern decomposition and analysis

Sentiment analysis for text documents and sentences

Comprehensive descriptive statistics for data exploration

Analyze system performance using throttle statistics, query logs, and resource metrics to identify bottlenecks and optimization opportunities