While relying on historical data is crucial for accurate forecasting models like RATSF, there are some drawbacks and limitations to consider:
Assumption of Similarity: Historical patterns may not always repeat themselves due to changing external factors or unforeseen events that disrupt traditional trends.
Lack of Adaptability: Over-reliance on past data may hinder the model's ability to adapt quickly to new information or emerging trends in real-time scenarios.
Data Quality Issues: Historical datasets may contain errors or biases that could impact the accuracy of forecasts if not properly addressed during preprocessing.
Limited Contextual Understanding: Historical data alone may not provide a comprehensive understanding of complex relationships between variables without additional contextual information.
To mitigate these limitations when relying heavily on historical data for forecasting, it is essential to incorporate dynamic updating mechanisms that account for evolving conditions and validate model outputs against real-time observations.