Analyzing Time Series
Comprehensive diagnostic analysis of time series data.
Install
npx skills add datawhalechina/agent-skills-with-anthropic --skill analyzing-time-series -g -a claude-code -y- Repository
- datawhalechina/agent-skills-with-anthropic1.6k
- Category
- Engineering
- Updated
- 2mo ago
- License
- See repository
- Context cost
- Lightabout 625 tokens
- Runs scripts
- Yes, review before use
What it does
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.
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)
