With time series data, the assumption of random distribution of error terms is usually violated.

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Time series data often exhibit patterns and trends, which means that the error terms are not truly random. They may be correlated with each other or with past values of the series. As a result, traditional statistical models that assume random distribution of error terms may not be appropriate for time series analysis. Time series models such as ARIMA and VAR take into account the non-randomness of the error terms and are therefore more suitable for analyzing time series data.

With Time Series Data, the Assumption of Random Distribution of Error