Temporal Clustering Invariance in Irregular Time Series
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Solution Overview
Problem
Current machine learning models face challenges in handling irregularly spaced time series data, particularly in healthcare and natural sciences, as they lack effective methods to exploit timing variances, leading to suboptimal performance and increased risk of overfitting.
Innovation Solution
The proposed solution involves generating multiple variants of irregular time series data through deterministic and stochastic coarsening techniques, which cluster data points based on a coarsening factor, creating coarsened time series that can be used for training and inference tasks, reducing overfitting and improving model robustness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If machine learning models use irregular time series data with variable timing intervals, then the models can capture real-world temporal patterns, but the models suffer from overfitting and reduced robustness
Solution Approach 1:
The patent applies parameter changes by transforming the time series data through multiple coarsening factors that modify the temporal resolution. Different coarsening factors (e.g., 0.5, 0.75, 1.0) create variants with different temporal densities, allowing the model to learn temporal patterns at multiple scales and improving robustness to timing variations.
Solution Approach 2:
The patent segments the irregular time series data into multiple coarsened variants, each representing a different level of temporal detail. This segmentation creates a family of related datasets that can be used for training and evaluation, reducing overfitting by preventing the model from memorizing specific timing patterns in the original data.
2Reliability
If machine learning models are trained on coarsened time series variants, then overfitting is reduced and robustness improves, but the data processing and model training complexity increases
Solution Approach 1:
The patent uses partial action by applying coarsening selectively rather than processing all possible variants. The system generates multiple coarsened variants with different coarsening factors and uses them in combination, rather than requiring exhaustive processing of every possible temporal transformation, thus improving training efficiency while maintaining robustness.
Solution Approach 2:
The coarsening framework serves multiple functions: it acts as a data augmentation technique, a regularization method, and a temporal feature extraction mechanism all in one. This multi-functionality reduces the need for separate processing pipelines for each objective, improving overall training efficiency.
3Measurement precision
If multiple coarsened variants of time series data are generated, then more training data is provided and model accuracy improves, but the computational resources required increase
Solution Approach 1:
The patent creates simplified copies of the original time series data through coarsening operations. These copied variants retain the essential temporal patterns while using fewer computational resources to process. The model learns from these lighter copies and applies the learned patterns to the full-resolution data during inference.
Solution Approach 2:
The coarsening process performs preliminary data preparation by pre-processing the time series into multiple variants before model training. This preliminary action organizes the data in a way that reduces computational burden during training, as the model learns temporal invariances from the coarsened versions that transfer to the full-resolution data.
4Ease of operation
If timestamp information is discarded to simplify processing, then handling irregular time series becomes easier, but temporal pattern recognition capability is lost
Solution Approach 1:
Instead of discarding timestamp information, the patent transforms it into a different dimension by creating multiple coarsening factors. This dimensional transformation preserves temporal information in a structured way that is compatible with standard sequence models, allowing the model to access temporal patterns through the coarsening factor dimension rather than through raw timestamps.
Data Source
AI summary
Techniques for generating multiple-resolutions of time series data are described. An input irregular time series having a plurality of data points is obtained, each data point of the plurality of data points including a timestamp and a feature vector. Based on the input irregular time series, multiple variant time series are generated. A data point in one of the variant time series is based in part on a combination of at least two data points of the input irregular time series. The multiple variant time series can then be used for machine learning tasks such as training a machine learning model or using a machine learning model to infer an output.