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Given new data, I trained the same model architecture and same hyperparameters (for example a random forest) as the current production model. How do I know that the new model that I trained is better than the current production model and make a decision to deploy the new model? Given that my problem scope is time series forecasting, the only way is to test on a timeline where both models were not trained on. For example, current production model was trained on data from year 2019 to 2020 and new model was trained on data from 2019 to 2021. We could possibly test it on 2018.

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