[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F3K490PYSX12FASD1SPMPR":3,"skill-same:01M6F3K490PYSX12FASD1SPMPR":63},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":35,"linkOnly":36,"markdownContent":37,"markdownBody":38,"frontMatter":39,"refName":40,"commitSha":41,"skillDirectory":42,"forksCount":43,"repositoryHtmlUrl":44,"repositoryHomepageUrl":45,"ownerHtmlUrl":46,"ownerBlogUrl":6,"ownerDescription":47,"updatedAt":48,"install":49,"resources":62},"01M6F3K490PYSX12FASD1SPMPR","analyzing-time-series",null,"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.","ComeOnOliver\u002Fskillshub","ComeOnOliver","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F43215937?v=4","01M6E5JNFECJAAVFE0W7SMWWD2","Haoran Shu","01M6E5JNNDQS7AJZDYFQ8TPZCK",66,"MIT","https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub\u002Fblob\u002Fmain\u002Fskills\u002Fdatawhalechina\u002Fagent-skills-with-anthropic\u002Fanalyzing-time-series\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34],"time series","analyzing","time","series","analyzing time series","csv","data","analysis","forecasting","use","trend","stationarity","recommendations","users","transform","understand","2026-11-10 10:12:30",false,"---\nname: analyzing-time-series\ndescription: 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.\n---\n\n# Time Series Diagnostics\n\nComprehensive diagnostic toolkit to analyze time series data characteristics before forecasting.\n\n## Input Format\n\nThe input CSV file should have two columns:\n- **Date column** - Timestamps or dates (e.g., `date`, `timestamp`, `time`)\n- **Value column** - Numeric values to analyze (e.g., `value`, `sales`, `temperature`)\n\n\n## Workflow\n\n**Step 1: Run diagnostics**\n\n```bash\npython scripts\u002Fdiagnose.py data.csv --output-dir results\u002F\n```\n\nThis 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.\n\n**Step 2: Generate plots (optional)**\n\n```bash\npython scripts\u002Fvisualize.py data.csv --output-dir results\u002F\n```\n\nCreates diagnostic plots in `results\u002Fplots\u002F` for visual inspection. Run after `diagnose.py` to ensure ACF\u002FPACF plots are synchronized with stationarity results. Column names are auto-detected, or can be specified with `--date-col` and `--value-col` options.\n\n**Step 3: Report to user**\n\nSummarize findings from `summary.txt` and present relevant plots. See `references\u002Finterpretation.md` for guidance on:\n- Is the data forecastable?\n- Is it stationary? How much differencing is needed?\n- Is there seasonality? What period?\n- Is there a trend? What direction?\n- Is a transform needed?\n\n## Script Options\n\nBoth scripts accept:\n- `--date-col NAME` - Date column (auto-detected if omitted)\n- `--value-col NAME` - Value column (auto-detected if omitted)\n- `--output-dir PATH` - Output directory (default: `diagnostics\u002F`)\n- `--seasonal-period N` - Seasonal period (auto-detected if omitted)\n\n## Output Files\n\n```\nresults\u002F\n├── diagnostics.json       # All test results and statistics\n├── summary.txt            # Human-readable findings\n├── diagnostics_state.json # Internal state for plot synchronization\n└── plots\u002F\n    ├── timeseries.png\n    ├── histogram.png\n    ├── rolling_stats.png\n    ├── box_by_dayofweek.png  # By day of week (if applicable)\n    ├── box_by_month.png      # By month (if applicable)\n    ├── box_by_quarter.png    # By quarter (if applicable)\n    ├── acf_pacf.png\n    ├── decomposition.png\n    └── lag_scatter.png\n```\n\n## References\n\nSee `references\u002Finterpretation.md` for:\n- Statistical test thresholds and interpretation\n- Seasonal period guidelines by data frequency\n- Transform recommendations\n\n## Dependencies\n\n`pandas`, `numpy`, `matplotlib`, `statsmodels`, `scipy`\n","# Time Series Diagnostics\n\nComprehensive diagnostic toolkit to analyze time series data characteristics before forecasting.\n\n## Input Format\n\nThe input CSV file should have two columns:\n- **Date column** - Timestamps or dates (e.g., `date`, `timestamp`, `time`)\n- **Value column** - Numeric values to analyze (e.g., `value`, `sales`, `temperature`)\n\n\n## Workflow\n\n**Step 1: Run diagnostics**\n\n```bash\npython scripts\u002Fdiagnose.py data.csv --output-dir results\u002F\n```\n\nThis 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.\n\n**Step 2: Generate plots (optional)**\n\n```bash\npython scripts\u002Fvisualize.py data.csv --output-dir results\u002F\n```\n\nCreates diagnostic plots in `results\u002Fplots\u002F` for visual inspection. Run after `diagnose.py` to ensure ACF\u002FPACF plots are synchronized with stationarity results. Column names are auto-detected, or can be specified with `--date-col` and `--value-col` options.\n\n**Step 3: Report to user**\n\nSummarize findings from `summary.txt` and present relevant plots. See `references\u002Finterpretation.md` for guidance on:\n- Is the data forecastable?\n- Is it stationary? How much differencing is needed?\n- Is there seasonality? What period?\n- Is there a trend? What direction?\n- Is a transform needed?\n\n## Script Options\n\nBoth scripts accept:\n- `--date-col NAME` - Date column (auto-detected if omitted)\n- `--value-col NAME` - Value column (auto-detected if omitted)\n- `--output-dir PATH` - Output directory (default: `diagnostics\u002F`)\n- `--seasonal-period N` - Seasonal period (auto-detected if omitted)\n\n## Output Files\n\n```\nresults\u002F\n├── diagnostics.json       # All test results and statistics\n├── summary.txt            # Human-readable findings\n├── diagnostics_state.json # Internal state for plot synchronization\n└── plots\u002F\n    ├── timeseries.png\n    ├── histogram.png\n    ├── rolling_stats.png\n    ├── box_by_dayofweek.png  # By day of week (if applicable)\n    ├── box_by_month.png      # By month (if applicable)\n    ├── box_by_quarter.png    # By quarter (if applicable)\n    ├── acf_pacf.png\n    ├── decomposition.png\n    └── lag_scatter.png\n```\n\n## References\n\nSee `references\u002Finterpretation.md` for:\n- Statistical test thresholds and interpretation\n- Seasonal period guidelines by data frequency\n- Transform recommendations\n\n## Dependencies\n\n`pandas`, `numpy`, `matplotlib`, `statsmodels`, `scipy`\n",{"name":5,"description":7},"main","def8531e65114c0fca8fb8551c1871ee0eed705c","skills\u002Fdatawhalechina\u002Fagent-skills-with-anthropic\u002Fanalyzing-time-series",24,"https:\u002F\u002Fgithub.com\u002FComeOnOliver\u002Fskillshub","https:\u002F\u002Fskillshub.wtf","https:\u002F\u002Fgithub.com\u002FComeOnOliver","AI & Blockchain Engineer\r\n","2026-06-24 22:07:19",{"source":8,"skillName":5,"commands":50,"prompt":60,"zipAvailable":61},[51,54,57],{"tool":52,"command":53},"npx","npx skills add ComeOnOliver\u002Fskillshub --skill analyzing-time-series",{"tool":55,"command":56},"bunx","bunx skills add ComeOnOliver\u002Fskillshub --skill analyzing-time-series",{"tool":58,"command":59},"pnpm","pnpm dlx skills add ComeOnOliver\u002Fskillshub --skill 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