Hi Chris! awesome video and explanations it really helped me. I wonder what's your opinion about ARIMA models vs ML models like Random Forest and XGBoost to do time series forecasting. From my point of view I could use lag values (Y_{t-1}, Y_{t-2}, etc) as features in a ML model, so I can feed the AR information in a ML model but I feel that the power of ML model may be not worth it sometimes because I lose the MA information, I can not feed (or I do not know how) the past errors of my model as a feature in a Random Forest let's say. What do you think?
It was a awesome lecture. I have not understood this for many years.Many thanks professor
Hi Chris! awesome video and explanations it really helped me. I wonder what's your opinion about ARIMA models vs ML models like Random Forest and XGBoost to do time series forecasting. From my point of view I could use lag values (Y_{t-1}, Y_{t-2}, etc) as features in a ML model, so I can feed the AR information in a ML model but I feel that the power of ML model may be not worth it sometimes because I lose the MA information, I can not feed (or I do not know how) the past errors of my model as a feature in a Random Forest let's say. What do you think?
Like si vienes de clase de Garza Galindo
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De hermanos ch1nga tu mais bro