Adaptive sparse time series encoding system

A system comprising:

• a processor set comprising one or more processors;
• a memory set storing instructions that, when executed by one or more processors of the processor set, cause the processor set to:
• obtain time series data comprising a plurality of data sequences with varying degrees of sparsity;
• categorize each data sequence into one of a plurality of sparsity pattern classifications based on analysis of data availability within a predetermined time window;
• apply different feature engineering techniques to data sequences based on their respective sparsity pattern classifications, wherein dense data sequences receive a first set of feature engineering techniques and sparse data sequences receive a second set of feature engineering techniques different from the first set;
• generate training datasets corresponding to each sparsity pattern classification;
• wherein the training datasets corresponding to dense data sequences comprise feature vectors containing moving averages and volatility measures, and the training datasets corresponding to sparse data sequences comprise feature vectors containing most recent measurement values and time-elapsed calculations, such that the training datasets corresponding to different sparsity pattern classifications contain feature vectors with different structural compositions;
• deploy separate machine learning models trained on the training datasets corresponding to each sparsity pattern classification, wherein to deploy includes to:
• receive new time series data for prediction;
• classify the new time series data into one of the plurality of sparsity pattern classifications;
• and
• route the new time series data to a corresponding deployed machine learning model based on the sparsity pattern classification of the new time series data to generate predictions.

A method comprising:

• receiving time series data having irregular sampling patterns and missing values across multiple data sequences;
• performing sparsity pattern analysis on each data sequence using a rolling window approach to classify each data sequence as a sparsity pattern classification of dense, sparse, or intermediate based on data availability metrics;
• selecting adaptive encoding strategies for each data sequence based on the sparsity pattern classification, wherein the adaptive encoding strategies comprise different feature extraction techniques optimized for the respective sparsity patterns;
• generating feature matrices from the time series data using the selected adaptive encoding strategies;
• wherein feature matrices generated for dense data sequences comprise moving averages and volatility measures, and feature matrices generated for sparse data sequences comprise most recent measurement values and time-elapsed calculations, such that the feature matrices for different sparsity pattern classifications have different structural compositions;
• training predictive models using the generated feature matrices to enable prediction on heterogeneous time series data with varying sparsity characteristics;
• and
• deploying the trained predictive models, wherein deploying includes:
• receiving new time series data for prediction;
• classifying the new time series data into one of the sparsity pattern classifications using the rolling window approach;
• and
• routing the new time series data to a corresponding trained predictive model based on the sparsity pattern classification of the new time series data to generate predictions.