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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