Supervisory neuron for continuously adaptive neural network

A method for real-time time series forecasting using a compound large codeword model comprising the steps of:

• receiving real-time time series data comprising a plurality of data types from diverse data sources;
• allocating codewords to each data input using a plurality of adaptive codebooks, wherein codewords are mapped to a corresponding codebook specific to each data type;
• fusing codewords of dissimilar data types together into a single codeword representation using a projection network that preserves inter-relationships between the dissimilar data types;
• processing the single codeword representation through a machine learning core comprising a transformer-based architecture, wherein the machine learning core comprises a plurality of operational neurons interconnected to form multiple local neural network regions;
• monitoring, by a plurality of supervisory neurons each supervisory neuron operatively connected to a respective local neural network region, activation data from the operational neurons in real-time during inference, the activation data comprising weights, biases, inputs, and outputs collected over multiple time cycle;
• performing, by the supervisory neurons, statistical analysis on the monitored activation data including temporal and spatial Fourier transforms to identify frequency components in neuron activations and wavelet analysis for multi-scale examination of activation patterns to identify patterns and anomalies in activation patterns over time;
• determining, based on the statistical analysis, one or more structural modifications to the respective local neural network regions to optimize performance for processing the fused codeword representations, wherein determining comprises maintaining a state-action value function updated based on the performance impact of past modifications and comparing current activation patterns against a historical record database implemented as a circular buffer storing past activation patterns, and wherein the structural modifications include at least one of neuron splitting, neuron pruning, neurogenesis, or connection modification;
• initiating implementation of the determined structural modifications during operation of the respective local neural network regions using gradient-based optimization techniques to smoothly transition the network structure while ensuring stability during modifications and without interrupting ongoing processing operations;
• monitoring performance of the respective local neural network regions before and after implementing the structural modifications;
• reverting modifications that do not improve performance;
• generating short-term forecasts based on a plurality of single codeword representations;
• outputting the short-term forecasts as a continuously updated time series prediction;
• and
• continuously updating the adaptive codebooks based on the real-time time series data to maintain prediction accuracy as data patterns evolve over time.