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I'm not sure I understand why you felt that you need to remove seasonality if you already have weather information as features for your model. Isn't season usua

I'm not sure I understand why you felt that you need to remove seasonality if you already have weather information as features for your model. Isn't season usually just used as a proxy for weather? If you have all the weather data you need (temperature, precipitation rate, type of precipitation, etc.), it seems a little weird that you subsequently only train the model on short time series to "remove the effect of seasonality".

iancassidy6 8y ago
Yes, you make very good points here about seasonality and weather. We have looked at training the model on a small window of flight dates vs randomly sampling across all dates and the former performed better in testing. We wanted to limit our modeling complexity to random forest and gradient boosting as an initial proof of concept here. Our plan is to retrain the current weather model weekly while monitoring performance, store the data, and then maybe look to using a more complex model like a neural net to train a model using data across all dates.