Nousot's demand forecasting solution splits your data into four major buckets in order to model each one separately.
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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.
They are challenging to model, difficult to scale, and painful to integrate. Credit https://www.nousot.com/resources/using-genai-to-completely-disrupt-traditional-platform-migrations-to-databricks-2/
Statistical, machine learning, and deep learning forecasting approaches each have their own unique pros and cons