Hierarchical time-series forecasting model optimization
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Solution Overview
Problem
Traditional time-series forecasting models face accuracy issues due to limited training data, reliance on sparse lower-level data, and mismatch between validation and test sets, leading to poor generalizability and hyperparameter optimization challenges.
Innovation Solution
The implementation of hierarchical knowledge distillation (HKD) where a teacher model trained on upper-level time-series data optimizes the hyperparameters of a student model trained on lower-level data, using predicted outputs for parameter tuning and aggregation to enhance forecasting accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional hold-out method is used to train time-series forecasting model, then model training process is simple, but model accuracy deteriorates due to limited training data and reliance on sparse lower-level data
Solution Approach 1:
The patent segments the time-series data into multiple hierarchical levels (e.g., daily, weekly, monthly aggregations) and trains separate models for each level. This allows the system to capture patterns at different granularities, improving overall accuracy while maintaining a manageable training process through modular level-specific models.
Solution Approach 2:
The patent introduces a hierarchical dimension to the data structure, organizing time-series data across multiple aggregation levels. This dimensional transformation enables the model to leverage both fine-grained detailed data and coarse-grained aggregated data, significantly improving accuracy without complicating the training process excessively.
2Ease of manufacture
If model is trained only on lower-level time-series data, then training process is straightforward, but model generalizability deteriorates due to mismatch between validation set and actual test set
Solution Approach 1:
The patent creates a multi-functional training approach where models at different hierarchical levels serve multiple purposes: lower-level models capture detailed patterns while upper-level models provide broader context and trends. This universal approach ensures the system can handle various data granularities and maintains generalizability across different test scenarios.
Solution Approach 2:
The patent performs preliminary aggregation of data into multiple hierarchical levels before training begins. This preliminary organization of data ensures that both detailed and aggregated perspectives are available from the start, preventing validation-set bias and improving generalizability to unseen test data.
3Measurement precision
If hyperparameters are tuned specifically for held-out validation set, then validation performance improves, but model generalizability to unknown test set deteriorates
Solution Approach 1:
The patent implements dynamic hyperparameter tuning across different hierarchical levels, where each level's model has its own optimized hyperparameters suited to its specific data characteristics. This dynamic approach allows each model to be tuned for its local validation set while maintaining compatibility with the broader hierarchical structure, preserving generalizability.
Solution Approach 2:
The patent changes the parameter optimization strategy by training models at multiple hierarchical levels with different aggregation granularities. Each level's model uses hyperparameters optimized for its specific data scale, allowing the system to adapt to various test scenarios without overfitting to a single validation set configuration.
Data Source
AI summary
An example operation may include one or more of storing a hierarchical time-series data set in memory, initially training a first time-series forecasting model based on a lower level of time-series data in the hierarchical data set, training a second time-series teaching forecasting model based on an upper level of time-series data from the hierarchical data set which includes an additional level of aggregation with respect to the lower level of time-series data, optimizing one or more parameters of the initially trained first time-series forecasting model based on predicted outputs from the trained second time-series forecasting model in comparison to predicted outputs from the initially trained first time-series forecasting model, and storing the modified first time-series forecasting model in the memory.