Selection of Stationarity Tests for Time Series Forecasting Using Reliability Analysis

Abstract

Stationarity is an essential concept in time series forecasting. A reliable stationarity test that yields unbiased test outcomes is vital as it is the gateway before a suitable forecasting model development. Renewable generation time series is inherently seasonal, comprising trend components, and often volatile. These characterizing facets alongside time series length tend to bias stationarity tests' outcomes. A critical comparison study to check the tests' reliability is carried out in this paper using different synthetic data required for the case-to-case analysis. Based on the tests' working, reliabilities are analyzed for different time series lengths and group sizes, varying from 200 to 1000 with an increment of 200. This provides information about changes in reliabilities of the tests for various time series lengths or group sizes. This comprehensive comparison report with a necessary set of well-illustrated figures, table, and thorough explanation of the obtained results is expected to help novice readers to select an apt combination of tests for stationarity check for renewable generation applications.