Machine Learning for Time Series Forecasting: Models, Benchmarks, and Challenges – A Case Study
TL;DR
This work provides a thorough analysis of deep learning architectures, contemporary machine learning algorithms, and traditional statistical models for time series forecasting, including ARIMA, Support Vector Regression, Random Forests, Long Short-Term Memory, and Transformer-based methods.
Abstract
In many fields, such as banking, healthcare, energy, and climate analysis, where precise predicting of future values is critical for making decisions, time series forecasting plays a critical role. With the quick development of machine learning methods, data-driven approaches have supplemented and frequently surpassed classic statistical models. This work provides a thorough analysis of deep learning architectures, contemporary machine learning algorithms, and traditional statistical models for time series forecasting. Critical analysis is done on important models including ARIMA, Support Vector Regression, Random Forests, Long Short-Term Memory (LSTM), and Transformer-based methods. The study also looks at evaluation criteria and benchmark datasets that are frequently used to compare performance. To illustrate how these models might be used in actual forecasting situations, a case study is provided. Problems including data quality, model interpretability, and computational complexity still exist despite tremendous advancements. Lastly, future directions are considered, such as explainable forecasting systems, automated machine learning, and hybrid models. An organized overview of contemporary developments and difficulties in time series forecasting is offered by this review.