KPSS

Executes KPSS unit root test to validate stationarity of time-series data in machine learning model.

Purpose: The Kwiatkowski-Phillips-Schmidt-Shin (KPSS) unit root test is utilized to ensure the stationarity of data within the machine learning model. It specifically works on time-series data to establish the order of integration, which is a prime requirement for accurate forecasting, given the fundamental condition for any time series model is that the series should be stationary.

Test Mechanism: This metric evaluates the KPSS score for every feature present in the dataset. Within the KPSS score, there are various components, namely: a statistic, a p-value, a used lag, and critical values. The core scheme behind the KPSS score is to test the hypothesis that an observable time series is stationary around a deterministic trend. If the computed statistic surpasses the critical value, the null hypothesis is dismissed, inferring the series is non-stationary.

Signs of High Risk: - High KPSS score represents a considerable risk, particularly if the calculated statistic is higher than the critical value. - If the null hypothesis is rejected and the series is recognized as non-stationary, it heavily influences the model’s forecasting capability rendering it less effective.

Strengths: - The KPSS test directly measures the stationarity of a series, allowing it to fulfill a key prerequisite for many time-series models, making it a valuable tool for model validation. - The logics underpinning the test are intuitive and simple, making it understandable and accessible for developers and risk management teams.

Limitations: - The KPSS test presumes the absence of a unit root in the series and does not differentiate between series that are stationary and those border-lining stationarity. - The test might show restricted power against specific alternatives. - The reliability of the test is contingent on the number of lags selected, which introduces potential bias in the measurement.