Multi-step time series forecasting for aviation and logistics demand using LSTM/GRU neural networks with NVIDIA CUDA acceleration.
The LSTM Demand Forecasting Engine applies recurrent neural network architectures — specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks — to predict multi-step future demand in aviation and logistics contexts.
Training is accelerated with NVIDIA CUDA, dramatically reducing experiment iteration time. The model learns complex temporal dependencies including seasonality, trend, and irregular event patterns from historical data.
An interactive Streamlit dashboard allows operators to visualize historical demand, confidence intervals around forecasts, and scenario comparisons. A REST API enables integration with operational planning and inventory systems for automated decision-making.
Loading time series, handling missing values, creating sliding windows, and splitting into train/val/test sets.
Building multi-layer LSTM and GRU models in Keras with dropout regularization for demand forecasting.
Configuring TensorFlow GPU device placement, mixed precision training, and monitoring GPU utilization.
Using Keras Tuner to optimize sequence length, number of layers, units, and dropout rates.
Implementing Monte Carlo dropout for uncertainty estimation and confidence interval generation.
Building a FastAPI endpoint and Streamlit dashboard for real-time forecast visualization and comparison.