Online learning of model parameters

A computer program product including one or more non-transitory computer readable storage mediums collectively storing program instructions that are executable by a processor or programmable circuitry to cause the processor or programmable circuitry to perform operations for enhancing performance of a trained machine learning model by additional machine learning model training after initial machine learning model deployment, comprising:

• obtaining a first target value in a target sequence and a feature vector corresponding to the first target value, the feature vector including a plurality of elements having new data patterns;
• dynamically adjusting the feature vector responsive to time-variant characteristic data of the target sequence and the feature vector to generate a modified feature vector by:
• reducing an absolute value of at least one element of the feature vector using a multiplication reducing factor between 0 to 1 responsive to an intercept component as at least one element of the feature vector being a constant feature;
• and
• updating the intercept component of the trained machine learning model to zero;
• iteratively optimizing the trained machine learning model by reducing processing time and processor resource utilization for continuous online training of the trained machine learning model trained for predicting an anomaly with newly detected time-variant characteristic data by applying training processes of a previous training to each data pattern in available historical data at each time step only once while regularizing model parameters of the trained machine learning model to maintain model parameters reflecting previous training including:
• generating an inverse Hessian matrix recursively, based on the previous training from a previous inverse Hessian matrix using a difference of a temporal inverse Hessian matrix and a proportion based on at least the feature vector, the modified feature vector, a transpose of the modified feature vector, the previous inverse Hessian matrix, and a regularization weight, the inverse Hessian matrix maintains the model parameters proportional to a weighted mean squared error between the target sequence and a future target value to avoid overfitting;
• updating model parameters of the trained machine learning model for continuous online training with only newly detected time-variant characteristic data from the target sequence using the modified feature vector and the recursively generated inverse Hessian matrix
• predicting a future target value in the targe t sequence based on an inner product of the updated model parameters and a feature vector corresponding to the future target value;
• and
• minimizing the weighted mean squared error between the target sequence and the future target value.

An apparatus for additional machine learning model training after initial machine learning model deployment, comprising:

• a processor or a programmable circuitry;
• and
• one or more computer readable mediums collectively including instructions that, when executed by the processor or the programmable circuitry, cause the processor or the programmable circuitry to:
• obtain a first target value in a target sequence and a feature vector corresponding to the first target value, the feature vector including a plurality of elements having new data patterns;
• dynamically adjust the feature vector responsive to time-variant characteristic data of the target sequence and the feature vector to generate a modified feature vector by:
• reducing an absolute value of at least one element of the feature vector using a multiplication reducing factor between 0 to 1 responsive to an intercept component as at least one element of the feature vector being a constant feature;
• and
• updating the intercept component of the trained machine learning model to zero;
• iteratively optimize the trained machine learning model by reducing processing time and processor resource utilization for continuous online training of the trained machine learning model trained for predicting an anomaly with newly detected time-variant characteristic data by applying training processes of a previous training to each data pattern in available historical data at each time step only once while regularizing model parameters of the trained machine learning model to maintain model parameters reflecting previous training including:
• generating an inverse Hessian matrix recursively, based on previous training from a previous inverse Hessian matrix using a difference of a temporal inverse Hessian matrix and a proportion based on at least the feature vector, the modified feature vector, a transpose of the modified feature vector, the previous inverse Hessian matrix, and a regularization weight, the inverse Hessian matrix maintains the model parameters proportional to a weighted mean squared error between the target sequence and a future target value to avoid overfitting;
• updating model parameters of the trained machine learning model for continuous online training with only newly detected time-variant characteristic data from the target sequence using the modified feature vector and the recursively generated inverse Hessian matrix;
• predicting a future target value in the target sequence based on an inner product of the updated model parameters and a feature vector corresponding to the future target value;
• and
• minimizing the weighted mean squared error between the target sequence and the future target value.