Long-term predictions often suffer from data scarcity, particularly in markets with infrequent trading or limited data availability. Sparse data poses challenges for machine learning models, as they require a sufficient amount of historical data to identify reliable patterns and make accurate predictions.

Long-term predictions may encounter limitations when there is a lack of predictors or historical patterns. Some markets or assets may have limited data on past behaviour, making it challenging for machine learning models to capture the drivers and dynamics that influence long-term price movements.

Long-term predictions in financial markets present unique challenges for machine learning models. The dynamic nature of markets, scarcity of data, and the absence of reliable predictors can limit the effectiveness of machine learning algorithms in making accurate long-term predictions.

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