If a model was selected to forecast page views back in 2017, it likely would perform well until late 2019. As we see in this chart, after 2020 the dynamics changed and the previously selected model might not handle the new dynamics well. This highlights the need for a continuously adaptive model search.
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If the demand is too random, there is sometimes nothing better than a flat line of zeroes, or ones. However, most clients/stakeholders do not like flat lines for forecasts.
Statistical, machine learning, and deep learning forecasting approaches each have their own unique pros and cons
This demonstration shows the process of a genetic algorithm applied to evolving the mechanics of walking. Each generation, the best of the previous generation are paired to create new variations of the best model. From https://rednuht.org/genetic_walkers/