Machine Learning Frameworks for Large-Scale Data Forecasting
TL;DR
This study reviews machine learning architectures for large-scale forecasting and proposes a scalable forecasting framework consisting of data collection, preprocessing, feature engineering, model training, forecasting, and performance evaluation, indicating that distributed machine learning frameworks improve forecasting accuracy while reducing computational costs.
Abstract
Machine learning has become a critical technology for large-scale data forecasting across industries such as finance, healthcare, transportation, manufacturing, energy, and e-commerce. While traditional forecasting methods like linear regression, ARIMA, and exponential smoothing perform well on smaller datasets, they struggle to manage the volume, velocity, and complexity of modern big data. Machine learning frameworks such as TensorFlow, Apache Spark MLlib, H2O.ai, Scikit-learn, and XGBoost offer scalable solutions by processing large datasets, identifying complex patterns, and generating accurate predictions through distributed computing.This study reviews machine learning architectures for large-scale forecasting, covering forecasting evolution, key algorithms, framework architectures, data processing pipelines, and evaluation methods. It proposes a scalable forecasting framework consisting of data collection, preprocessing, feature engineering, model training, forecasting, and performance evaluation. Findings indicate that distributed machine learning frameworks improve forecasting accuracy while reducing computational costs. Ensemble learning and gradient boosting techniques demonstrate superior performance, scalability, and robustness compared to traditional methods. The study concludes that machine learning frameworks provide a strong foundation for large-scale forecasting and will play an increasingly important role in future predictive applications.