It is generally not possible to accurately predict the future with a fixed dataset and computational budget, as the future is inherently uncertain and can be influenced by a wide range of factors. However, statistical and machine learning models can be used to make informed predictions about the future based on historical data and assumptions about the underlying patterns in the data.

The best model for making predictions about the future will depend on the specific problem at hand and the characteristics of the dataset. Some factors to consider when selecting a model for predicting the future include:

The complexity of the problem: If the problem is very complex, with many interacting factors that may influence the outcome, a more complex model may be necessary to capture these interactions.

The size and quality of the dataset: A larger and higher-quality dataset may allow for the use of a more complex model, which may improve the accuracy of the predictions.

The computational resources available: If the computational budget is limited, it may be necessary to use a simpler model that can be fit and evaluated more quickly.