XGBoost for TIme Series Dataset | Demand Forecasting | Machine-Learning with Python

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Gradient Boosting can be adapted for time-series forecasting problems with some specific considerations and modifications. Time-series data has a temporal structure, and the goal is to predict future values based on historical observations.

We can restructure this time series dataset as a supervised learning problem by leveraging the value at the prior time step to forecast the value at the subsequent time-step.

Feature engineering:

Here, you'll create new features based on your original timestamp and Date-wise consumption data.

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Short and informative video on XGBoost for time series forecasting. Thanks that's what I'm looking for.

GurpreetSingh-dpxe

Thank you for this video, Can we use the same method in random forest regressor? or support vector regressor?

imane-hxkp

Thanks for sharing. Would you mind sharing which plug-in that you are using in VS Code which makes it so bright on the screen.

adilmajeed

How do we create a cross validation pipeline? Eg. A sliding window CV and tune parameters then choose best model.

Septumsempra

Your mic level is too high. This causes the viewer to hear distortion in nearly every word you speak. Your content is very valuable, so please take care of this!

mkvalor

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