Comprehensive diagnostic analysis of time series data. Use when users provide CSV time series data and want to understand its characteristics before forecasting - stationarity, seasonality, trend, forecastability, and transform recommendations.
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Comprehensive diagnostic toolkit to analyze time series data characteristics before forecasting.
The input CSV file should have two columns:
date, timestamp, time)value, sales, temperature)Step 1: Run diagnostics
python scripts/diagnose.py data.csv --output-dir results/
This runs all statistical tests and analyses. Outputs diagnostics.json with all metrics and summary.txt with human-readable findings. Column names are auto-detected, or can be specified with --date-col and --value-col options.
Step 2: Generate plots (optional)
python scripts/visualize.py data.csv --output-dir results/
Creates diagnostic plots in results/plots/ for visual inspection. Run after diagnose.py to ensure ACF/PACF plots are synchronized with stationarity results. Column names are auto-detected, or can be specified with --date-col and --value-col options.
Step 3: Report to user
Summarize findings from summary.txt and present relevant plots. See references/interpretation.md for guidance on:
Both scripts accept:
--date-col NAME - Date column (auto-detected if omitted)--value-col NAME - Value column (auto-detected if omitted)--output-dir PATH - Output directory (default: diagnostics/)--seasonal-period N - Seasonal period (auto-detected if omitted)results/
├── diagnostics.json # All test results and statistics
├── summary.txt # Human-readable findings
├── diagnostics_state.json # Internal state for plot synchronization
└── plots/
├── timeseries.png
├── histogram.png
├── rolling_stats.png
├── box_by_dayofweek.png # By day of week (if applicable)
├── box_by_month.png # By month (if applicable)
├── box_by_quarter.png # By quarter (if applicable)
├── acf_pacf.png
├── decomposition.png
└── lag_scatter.png
See references/interpretation.md for:
pandas, numpy, matplotlib, statsmodels, scipy
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Analyze weekly marketing campaign performance data across channels. Use when analyzing multi-channel digital marketing data to calculate funnel metrics (CTR, CVR) and compare to benchmarks, compute cost and revenue efficiency metrics (ROAS, CPA, Net Profit), or get budget reallocation recommendations based on performance rules.
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Generate educational practice questions from lecture notes to test student understanding. Use when users request practice questions, exam preparation materials, study guides, or assessment items based on lecture content.
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