Description

This Master Prompt investigates advanced deep learning architectures for forecasting complex temporal systems characterised by nonlinear relationships, long-range dependencies, multiple variables, and large-scale datasets. It covers artificial neural networks, multilayer perceptrons, convolutional neural networks, recurrent neural networks, long short-term memory networks, gated recurrent units, temporal convolutional networks, encoder–decoder architectures, attention mechanisms, transformer-based forecasting models, graph-enhanced neural networks, and emerging time series foundation models. The theme examines temporal representation learning, sequence-to-sequence forecasting, multi-horizon prediction, positional encoding, patch-based representations, trend–seasonality decomposition, long-range dependency modelling, transfer learning, domain adaptation, knowledge distillation, and computationally efficient architectures. It emphasises systematic architecture design, training strategies, hyperparameter optimisation, generalisation, interpretability, benchmarking, scalability, and deployment considerations for short-, medium-, and long-term forecasting across scientific, industrial, financial, and environmental applications.