# Time Series Analysis

Temporal pattern detection and forecasting.

- Repository: https://github.com/nimrodfisher/data-analytics-skills (512 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-09-25
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add nimrodfisher/data-analytics-skills --skill time-series-analysis -g -a claude-code -y`
Codex: `npx skills add nimrodfisher/data-analytics-skills --skill time-series-analysis -g -a codex -y`
Cursor: `npx skills add nimrodfisher/data-analytics-skills --skill time-series-analysis -g -a cursor -y`
Gemini CLI: `npx skills add nimrodfisher/data-analytics-skills --skill time-series-analysis -g -a gemini-cli -y`

## Description

Temporal pattern detection and forecasting. Use when analyzing trends over time, detecting seasonality, identifying anomalies in time series, or building simple forecasting models for planning.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Time Series Analysis

# When to use
- Building a forecast for operational planning (staffing, inventory, infrastructure capacity)
- Identifying whether a trend is genuine or driven by seasonality
- Detecting anomalies in a metric stream (traffic spikes, revenue dips, error rate surges)
- Providing a "what would have happened" baseline for measuring initiative impact
- Presenting year-over-year growth in a way that accounts for seasonal patterns

# Process
1. **Load and inspect the time series**, confirm regular intervals (fill gaps if needed), check for obvious data quality issues (negative values, zeros in non-zero series), and identify the natural granularity (daily, weekly, monthly).
2. **Test for stationarity**, run an ADF test. If non-stationary (trend or seasonality present), note this, it informs decomposition and model choice rather than blocking analysis. See `references/ts_patterns_guide.md`.
3. **Decompose into components**, separate the time series into trend, seasonal, and residual using additive or multiplicative decomposition. Measure the strength of each component (0, 1). Strong seasonality (>0.6) means raw values are misleading without seasonal adjustment.
4. **Detect anomalies**, flag points more than 3 standard deviations from the rolling median. Investigate the top 5 anomalies against the event log (product releases, campaigns, incidents). Use `scripts/ts_analyzer.py --detect-anomalies`.
5. **Fit a forecast model**, fit an ARIMA model (or simpler moving average if data is short). Validate on a held-out 20% test set and report MAPE. Generate point estimates and 95% confidence intervals for the forecast horizon.
6. **Produce the analysis report**, summarise trend direction and strength, seasonal patterns and their business implications, anomaly findings, and the forecast with uncertainty. Use `assets/ts_report_template.md`.

The rest: https://github.com/nimrodfisher/data-analytics-skills/blob/HEAD/03-data-analysis-investigation/time-series-analysis/SKILL.md