Addressing Challenges in Time Series Forecasting: A Comprehensive Comparison of Machine Learning Techniques
This work provides guidance for practitioners in selecting forecasting algorithms based on data characteristics, but it is incremental as it compares existing methods without introducing new techniques.
The paper tackled the problem of selecting effective machine learning algorithms for time series forecasting by comparing various methods against ARIMA across datasets with different challenges, finding that certain algorithms outperform others in accuracy for long-term predictions.
The explosion of Time Series (TS) data, driven by advancements in technology, necessitates sophisticated analytical methods. Modern management systems increasingly rely on analyzing this data, highlighting the importance of effcient processing techniques. State-of-the-art Machine Learning (ML) approaches for TS analysis and forecasting are becoming prevalent. This paper briefly describes and compiles suitable algorithms for TS regression task. We compare these algorithms against each other and the classic ARIMA method using diverse datasets: complete data, data with outliers, and data with missing values. The focus is on forecasting accuracy, particularly for long-term predictions. This research aids in selecting the most appropriate algorithm based on forecasting needs and data characteristics.