deerishi / ensemble-of-sarima-random-forests-and-gradient-boosting-trees

In this Project I use the Kaggle Bike sharing dataset to predict the sales of bike given a Multivariate Time series. I model the multivariate data using ensemble of Random Forests and Gradient Boosted trees. After that the residuals of the model are fit with an ARMA/ARIMA/SARIMA model and later forecasted. The residuals are later added back to the predicted values

Home Page:https://deerishi.github.io/ensemble-of-sarima-random-forests-and-gradient-boosting-trees/

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ensemble-of-sarima-random-forests-and-gradient-boosting-trees

In this Project I use the Kaggle Bike sharing dataset to predict the sales of bike given a Multivariate Time series. I model the multivariate data using ensemble of Random Forests and Gradient Boosted trees. After that the residuals of the model are fit with an ARMA/ARIMA/SARIMA model and later forecasted. The residuals are later added back to the predicted values

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In this Project I use the Kaggle Bike sharing dataset to predict the sales of bike given a Multivariate Time series. I model the multivariate data using ensemble of Random Forests and Gradient Boosted trees. After that the residuals of the model are fit with an ARMA/ARIMA/SARIMA model and later forecasted. The residuals are later added back to the predicted values

https://deerishi.github.io/ensemble-of-sarima-random-forests-and-gradient-boosting-trees/


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