Time Series Analysis

Overview

Time Series Analysis builds a forecast for operational planning, checks whether a trend is genuine or seasonal, detects anomalies in a metric stream, or measures the real impact of an initiative against a baseline.

What it covers

• Confirms regular intervals and checks for obvious data quality issues before analysis starts.
• Tests for stationarity, then decomposes the series into trend, seasonal, and residual components with a strength score for each.
• Flags points more than 3 standard deviations from the rolling median and cross-checks the top anomalies against your event log.
• Fits an ARIMA model (or a simpler moving average for short series), validates on a held-out set, and reports the error rate.
• Produces point estimates with 95% confidence intervals and a report covering trend, seasonality, anomalies, and the forecast.

Part of Data Analytics Skills

This is one of 31 skills in the Data Analytics Skills library, in the Data Analysis & Investigation group.

Installation

1. Clone the repo once: git clone https://github.com/nimrodfisher/data-analytics-skills.git
2. Copy just this skill: cp -r data-analytics-skills/03-data-analysis-investigation/time-series-analysis ~/.claude/skills/ (or ~/.agents/skills/ for Codex)
3. Ask your agent something like "forecast next quarter's signups and flag any anomalies" and it activates on its own.

See the Data Analytics Skills entry for the full library.