ARIMA Models

Learning the Ljung-Box Test: Detecting Autocorrelation in Time Series Data

Introduction: Defining the Ljung-Box Test The Ljung-Box test is recognized as a fundamental diagnostic procedure within time series analysis. This critical statistical tool, developed by statisticians Greta M. Ljung and George E.P. Box, provides a formal mechanism to determine if the autocorrelations of a data series, across a specified range of lags, are collectively distinguishable […]

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Learning the Augmented Dickey-Fuller (ADF) Test for Time Series Stationarity in R

The Foundation: Why Time Series Stationarity Matters A time series is central to quantitative finance, econometrics, and predictive analytics. For effective statistical modeling, such as using ARIMA or GARCH models, the data must satisfy a critical statistical prerequisite: stationarity. A process is classified as stationary if its statistical characteristics—specifically the mean, variance, and the autocorrelation

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