Time Series Diagnostics

Comprehensive diagnostic toolkit to analyze time series data characteristics before forecasting.

Input Format

The input CSV file should have two columns:

• Date column - Timestamps or dates (e.g., date, timestamp, time)
• Value column - Numeric values to analyze (e.g., value, sales, temperature)

Workflow

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:

• Is the data forecastable?
• Is it stationary? How much differencing is needed?
• Is there seasonality? What period?
• Is there a trend? What direction?