Unveiling Time Series Forecasting: A Deep Dive into Machine Learning Approaches

Time series forecasting has become essential in various industries, including finance, energy, healthcare, and environmental science, where accurate predictions drive strategic decision-making. This work examines deep learning-based time-series forecasting models for non-stationary and non-linear data, focusing on LSTM based hybridized model that enhance long-term dependencies and predictive accuracy. This also explores decomposition-based hybrid models that uses trend-seasonality and wavelet methods, along with advanced models like Temporal Fusion Transformers (TFT) and LSTM-CNN combinations. Despite their success, these models face challenges like computational complexity, overfitting, and limited adaptability to extreme events. Key research gaps include real-time data integration, interpretability, and computational efficiency. Future research directions should enhance model robustness, incorporate external variables, and develop lightweight architectures for real-time forecasting. Closing these gaps will facilitate more efficient forecasting models, enhancing decisions in critical domains like energy, finance, and healthcare.