Univariate Time Series Forecasting

Univariate time series forecasting aims to predict future values of a single time-dependent variable, crucial for diverse applications ranging from finance to energy management. Current research emphasizes improving model accuracy through advanced architectures like deep learning models (e.g., LSTMs, Transformers) and refined techniques such as optimal starting point selection, data augmentation, and ensemble methods, while also addressing explainability and robustness challenges. These advancements enhance the reliability and interpretability of forecasts, leading to more informed decision-making across various sectors and contributing to a deeper understanding of temporal dynamics in complex systems.