Implicit Neural Representation for Interpretable Time Series
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Solution Overview
Problem
Current methods for generating time series data lack effective mechanisms for data augmentation, especially in scenarios with privacy or proprietary concerns, and are limited by spatial resolution in traditional discrete representations, which hinders the generation of interpretable time series.
Innovation Solution
The use of implicit neural representations (INRs) with sinusoidal representation networks and hypernetworks to generate interpretable time series by encoding continuous functional relationships, allowing for interpolation and extrapolation beyond the given data points, incorporating trend and seasonality components.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional discrete representations (data grids) are used for time series encoding, then spatial resolution is fixed and discretization artifacts occur, but if continuous representations are used, then resolution independence and interpolation capability are achieved
Solution Approach 1:
The patent replaces traditional discrete grid-based mechanical representation with a continuous neural field representation. Instead of using fixed spatial grids that require discretization, the invention employs implicit neural representations that continuously map input coordinates to output values, eliminating discretization artifacts and enabling resolution-independent time series encoding.
2Reliability
If more data points are collected to improve model generalization, then model performance improves, but data privacy and proprietary concerns prevent data sharing
Solution Approach 1:
The patent creates synthetic copies of time series data through implicit neural representations. By training the model on available data and then generating synthetic samples from the learned continuous representation, the system effectively creates copies that preserve statistical properties and temporal patterns without requiring access to or sharing of the original sensitive data, thus improving generalization while maintaining privacy.
3Measurement precision
If implicit neural representations with sinusoidal activation functions are used, then high-frequency detail encoding accuracy improves, but training complexity increases
Solution Approach 1:
The patent changes the activation function parameter from standard nonlinear activations to sinusoidal activation functions. This parameter change enables the network to naturally represent high-frequency components in the time series data, as sinusoidal functions can capture periodic variations and fine-grained temporal patterns more effectively, thereby improving high-frequency encoding accuracy despite increased training complexity.
Data Source
AI summary
A method and a system for using implicit neural representations for generation of interpretable time series are provided. The method includes: receiving time series information, such as pairings of time coordinate values with time series signal values, that relates to an event sequence; generating, based on the time series information, an implicit neural representation of the event sequence that includes a plurality of embedded values and a corresponding plurality of weights; and using the implicit neural representation to predict at least one item of information that relates to the event sequence and is not included in the received time series information, such as an interpolation or an extrapolation of the time series.