Using XGBoost for Time Series Forecasting

As you learned in the preceding chapters, XGBoost is a powerful ensemble learning technique that builds a series of decision trees and aggregates their predictions to produce robust, accurate models. XGBoost excels in handling various regression and classification tasks by capturing complex, non-linear relationships within the data. This chapter focuses specifically on leveraging XGBoost for time series forecasting—also referred to as time series prediction, which means the same. Forecasting is a critical application in fields such as finance, supply chain management, and energy demand planning.

Time series forecasting presents unique challenges due to the sequential and temporal nature of the data. Unlike traditional supervised learning tasks where observations are independent and identically distributed, time series data points are intrinsically ordered and often exhibit trends, seasonality, and autocorrelation. XGBoost, though...