BOOSTING MACHINE LEARNING METHODS FOR NON-STATIONARY TIME SERIES

Abstract

Most machine learning algorithms assume that the data they are trained on is stationary, meaning that its distribution does not change over time. However, many time series we encounter in the real world are non-stationary, that is, their distribution changes over time. This can make forecasting based on this data difficult.
The non-stationarity of time series can be caused by many factors, such as seasonal fluctuations, trends, cyclicality and shocks. These factors can make forecasting non-stationary time series difficult because they can change the distribution of the data and make it less predictable.
Boosting machine learning algorithms is one approach to solving this problem. They work by training a sequence of weak models and then combining their results to produce a strong model. This can be effective for forecasting non-stationary time series because a strong model can compensate for the limitations of weak models.

Boosting machine learning methods are a powerful tool for effectively forecasting non-stationary time series data. They enable the creation of strong models that are significantly more accurate than individual weak models. One of the most well-known boosting methods for non-stationary time series is XGBoost, which utilizes innovative features such as gradient descent with adaptive learning rates and regularization to achieve high accuracy. This article discusses the advantages of using boosting methods for non-stationary time series forecasting, including high accuracy, resistance to overfitting, and versatility in handling various tasks. However, it should be noted that boosting methods also have certain drawbacks, such as training complexity and high cost. Despite these limitations, boosting machine learning methods remain a powerful tool for addressing a wide range of non-stationary time series forecasting tasks, including stock price prediction, weather forecasting, and sales forecasting.