In the realm of time series forecasting, how can one assess the predictive potential of a given time series? While traditional methods involve checking for stationarity and white noise characteristics, it seems that newer techniques like deep learning may not require these steps. In essence, what attributes define a time series as suitable for predictive modeling?
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