analyzing-time-series

To check time-series forecasting readiness, diagnose stationarity, seasonality, trend, and autocorrelation. This Skill analyzes CSV inputs to evaluate data quality, apply ADF/KPSS tests, and recommend necessary transforms like differencing or Box-Cox.

Detecting seasonality and trend in a CSV time-series requires analyzing autocorrelation patterns and applying statistical tests. This Skill estimates seasonal periods, assesses trend strength, and generates diagnostic plots to reveal underlying data patterns.

Determining the differencing order for ARIMA models involves running stationarity tests on your time-series. This Skill applies ADF and KPSS tests to your CSV data and provides explicit guidance on the recommended differencing order.

You need to transform time-series data before building a SARIMA model if variance is non-constant. This Skill performs Box-Cox analysis and variance-stability checks to recommend exact transformations required for stationarity.

You can use pandas and statsmodels to assess time-series forecastability by examining autocorrelation and applying Ljung-Box tests. This Skill leverages these libraries to evaluate data quality, distribution, and generate ready-to-use diagnostics.

Time-series forecasting models perform poorly on non-stationary data due to unhandled trends, seasonality, or non-constant variance. This Skill diagnoses these issues in your CSV data and recommends transforms to achieve stationarity.