Demand forecasting is not a single stationary prediction problem, but a large and continually changing family of related regression tasks. Retail and manufacturing systems must forecast demand simultaneously for many stores, products, warehouses, and time horizons, while the statistical properties of individual series change over time. Modern global forecasting models benefit from sharing information across many time series, but their computational structure is normally fixed once training begins. A fixed architecture can force highly heterogeneous tasks through the same representation, creating negative transfer, interference between tasks, and insufficient capacity when new demand regimes appear. This project will develop and evaluate Fully Dynamic Neural Networks (FD-NNs) for continual demand forecasting. The central idea is to make both the training graph and the inference graph structurally adaptive. The proposed forecaster contains embeddings for categorical entities such as store and stock-keeping-unit identifiers, pools of candidate hidden modules at each network depth, and a pool of regression heads. Each forecasting task is assigned a persistent task-specific route through these module pools. The model is first globally pretrained on all available tasks to learn a shared initialization. When a new task is encountered, a small number of adaptation batches are used to evaluate the compatibility of existing modules. A weight-gradient alignment criterion determines whether the task should reuse an existing module or whether the architecture should grow by adding a new hidden block or output head. The selected route is stored in the model and reused during later inference, so structural adaptation becomes part of the learned model state rather than a temporary training decision. The project will study the interaction between dynamic structural growth, multi-task learning, and continual learning at a scale of hundreds to more than one thousand forecasting tasks. After sequential task adaptation, refinement passes will revisit known tasks, reselect the best routes among the modules already created, and consolidate knowledge without further architecture expansion. Experiments will compare the proposed approach with conventional machine-learning baselines and strong fixed-architecture neural forecasting methods. The evaluation will measure forecasting accuracy, transfer between related tasks, resistance to catastrophic forgetting, routing stability, model growth, computational cost, and the trade-off between shared and task-specific capacity. The intended outcome is a reproducible forecasting framework that can share knowledge when transfer is beneficial, isolate tasks when gradients conflict, and expand only when existing capacity is insufficient.