What are the two significant factors affecting the success of an ARIMA model?

The factors that significantly affect the success of an ARIMA model critically relate to how well the underlying assumptions of the model hold true. In the context of the options provided, high correlation in autoregression and low variance in errors are key characteristics that can enhance the model's efficacy.

High correlation in autoregression suggests that past values of the time series have a significant influence on current values. This correlation allows the ARIMA model to effectively capture the dynamics of the series, leading to more accurate forecasts. If the autoregressive terms are well-correlated with the target variable, it allows the model to extrapolate future values based on observed trends.

Low variance in errors indicates that the model's predictions are consistently close to the actual values. This characteristic is essential as it reflects the model's reliability. Lower variance in errors typically results in more stable forecast performance, meaning the model is less likely to produce outlier predictions and provides a smoother estimation of future values.

Both of these insights underscore the fact that an ARIMA model's predictive power is contingent upon the statistical properties of the time series data it is being fit to, making this choice the most pertinent.