Time Series & Telemetry calculator

Augmented Dickey-Fuller (ADF) stationarity test calculator

Test whether a time series possesses a unit root and is non-stationary using the Augmented Dickey-Fuller (ADF) test.

Run this test live in StatLab npm i @statlab/core

When to use it

Use prior to modeling or forecasting time-series telemetry data.

Required Inputs

  • Time series data vector Y_t
  • Lag order choice
  • Trend component (constant / linear trend / none)

Mathematical Formula

Δy_t = α + βt + γ y_{t-1} + δ_1 Δy_{t-1} + ... + ε_t

Reporting Cautions

  • Rejection of null hypothesis (p < .05) implies the series IS stationary.
  • Selecting an incorrect lag length can distort test size and power.

Code Snippets (Python, R, TypeScript)

Python (SciPy / Statsmodels) 
from statsmodels.tsa.stattools import adfuller
res = adfuller(time_series)
print(f"ADF={res[0]:.4f}, p={res[1]:.4f}")
R Language 
library(tseries)
adf.test(time_series)
TypeScript (@statlab/core) 
import { adfTest } from '@statlab/core';
const res = adfTest(timeSeries);

Developer Use Cases & Production Integrations

  • Verifying stationarity of CPU/memory metrics before applying ARIMA/telemetry models in VoxelPulse.
  • Testing whether performance metric trends represent genuine drift vs stationary noise.

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