Time-Series Analysis

From decomposition to auto ARIMA fitting — all built in.

Stationarity tests, cointegration, CCF, and other diagnostics in one flow. Include exogenous variables and get prediction intervals out of the box.

At a glance

Common use

Demand forecasting · KPI monitoring · Period-level impact

Engine

pmdarima auto_arima · statsmodels SARIMAX

Required columns

Time (datetime / Y·M·D) + at least one numeric series

Recommended length

≥ 30 time points; seasonal analysis needs ≥ 2 cycles

Plan

PREMIUM plan and above

Workflow

  1. 1Recognise time column + datetime conversion
  2. 2Derive features (weekday · month · quarter · holiday flag)
  3. 3Time-series visualisation + correlation matrix
  4. 4Trend/seasonal decomposition
  5. 5Stationarity tests (ADF · KPSS) + auto-differencing
  6. 6Auto ARIMA / SARIMAX order selection (auto_arima)
  7. 7Forecast + confidence intervals + diagnostic plots

Supported analyses

  • Time-series visualisation

    Compare multiple series simultaneously and spot patterns

  • Decomposition

    Auto-decompose into trend · seasonality · residual

  • Stationarity tests

    ADF / KPSS; auto-difference when non-stationary

  • CCF · cointegration

    Lead-lag relationships + long-run equilibrium

  • ARIMA · SARIMAX

    auto_arima finds optimal order, supports exogenous variables

  • Forecast + diagnostics

    Forecast + 95% CI + Ljung-Box / Jarque-Bera diagnostics

Use cases

  • Monthly revenue forecast

    5-year monthly revenue + ad spend (exogenous) → 12-month forecast via SARIMAX.

  • Call-centre volume forecast

    Hourly call volume + holiday flag → next-week forecast for staffing.

  • KPI anomaly monitoring

    Out-of-CI time points trigger alerts.

What you get

  • Decomposition chart (trend · seasonal · residual)
  • Stationarity test results + differencing log
  • ARIMA order + fit stats (AIC · BIC · Log-Likelihood)
  • Forecast chart + confidence intervals
  • Residual diagnostics + auto-generated paper