Analyzing Time Series Agent Skill

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.

14k tokens

context cost

the whole folder, loaded on every use

files

ships runnable scripts

copies elsewhere

how many repositories repackaged it

1436

stars on the repo

on the repository, not the skill itself

Install

one command, takes just this skill from the repository

npx skills add https://github.com/datawhalechina/agent-skills-with-anthropic --skill analyzing-time-series

The instruction itself

7 sections, as written by the author

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

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How to use it

Copy the folder

Take datawhalechina/analyzing-time-series from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.