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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