Short Term Forecasting

Short-term forecasting aims to predict values of time series data—like weather patterns, energy consumption, or stock prices—over short time horizons. Current research heavily emphasizes the use of deep learning models, including variations of LSTMs, Transformers, and MLPs, often enhanced by techniques like wavelet denoising and frequency-domain analysis to improve accuracy and efficiency. These advancements are crucial for optimizing resource allocation in various sectors, from renewable energy integration to financial markets and public health management, and are driving the development of novel model architectures and data processing methods. Furthermore, research is actively exploring the balance between computational cost and forecast accuracy, particularly in resource-constrained environments like IoT applications.