Individual skill

time-series-analysis

Analyze time-ordered data — decomposition, stationarity, forecasting models, changepoint detection, and proper temporal validation. Use when the order of observations matters.

skills/time-series-analysis/SKILL.md

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# Time Series Analysis

Time series data has memory: today's value depends on yesterday's. Analysis must respect that — 
in modeling (autocorrelation, seasonality, trends), in validation (never shuffle time), and in 
forecasting (uncertainty grows with horizon).

## Overview

Start by understanding the series: plot it, decompose into trend/seasonality/residuals, check 
stationarity. Model the structure you find — classical methods (ARIMA family, exponential 
smoothing) for well-behaved series, machine learning for complex patterns with enough data. 
Validate with time-based splits only: train on the past, test on the future, rolling forward. 
Forecast with intervals, because point forecasts without uncertainty are wishes.