Evaluating the Effectiveness of Dimensionality Reduction on Machine Learning Algorithms in Time Series Forecasting

Time series analysis is a critical task across various scientific and industrial domains, enabling the extraction of valuable insights from temporal data. High dimensionality of time series data can lead to computational inefficiencies and increased complexity. Dimensionality reduction techniques play a crucial role in handling the high-dimensional nature of time series data essential information while reducing computational complexity. This study explores the impact of various dimensionality reduction techniques and machine learning models on enhancing the accuracy and efficiency of time series datasets. The effectiveness of different dimensionality reduction methods is evaluated based on their ability to simplify data while preserving essential features. Subsequently, several machine learning models—such as Autoregressive Integrated Moving Average, k-Nearest Neighbors, Random Forest, Least Absolute Shrinkage and Selection Operator, and Extreme Gradient Boosting are applied to the transformed data. The findings reveal that the choice of dimensionality reduction technique significantly influences the performance of these models. Certain methods excel at uncovering underlying patterns and improving predictive accuracy, while others offer computational advantages. These results highlight the importance of selecting an appropriate combination of dimensionality reduction and machine learning techniques based on the specific characteristics of the time series data. This can contributes to a deeper understanding of how these methods can be effectively integrated, thereby enhancing decision-making in areas such as finance, meteorology, and operational planning.