https-deeplearning-ai/analyzing-time-series

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?
• Is a transform needed?

Script Options

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)

Output Files

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

References

See references/interpretation.md for:

• Statistical test thresholds and interpretation
• Seasonal period guidelines by data frequency
• Transform recommendations

Dependencies

pandas, numpy, matplotlib, statsmodels, scipy