Modulated Auto-Decoding Framework for Time Series Imputation
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Solution Overview
Problem
Current time series imputation methods, especially for multivariate data, face limitations in capturing complex temporal dependencies and handling irregularly sampled data, often resulting in biased outputs due to oversimplified assumptions and high computational costs.
Innovation Solution
A modulated auto-decoding framework built upon implicit neural representations, specifically using a sinusoidal representation network (SIREN) with a modulation network and hypernetwork, generates network weights and modulates sine activation amplitudes to impute missing values in time series data, allowing for more accurate and generalizable predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional neural networks are used for time series imputation, then the model can process data, but it suffers from spectral bias and cannot adequately capture high-frequency details
Solution Approach 1:
The patent transforms the activation function parameter from standard ReLU to sinusoidal activation functions (SIREN), fundamentally changing the frequency characteristics of the neural network. This parameter change enables the model to capture high-frequency details in time series data while maintaining smooth continuous representations, directly resolving the spectral bias limitation of traditional networks
Solution Approach 2:
The patent introduces periodic sinusoidal activation functions that operate in oscillatory patterns rather than monotonic transformations. These periodic activations enable the network to represent temporal patterns with varying frequencies, allowing accurate capture of both low-frequency trends and high-frequency fluctuations in the time series data
2Measurement precision
If SIREN with periodic activations is used to capture high-frequency details, then imputation accuracy improves, but computational cost increases significantly
Solution Approach 1:
The patent pre-computes and stores sinusoidal basis functions and their derivatives at training time, creating lookup tables that are reused during inference. This preliminary action eliminates redundant computations of trigonometric functions during forward propagation, significantly reducing computational cost while maintaining the ability to capture high-frequency details
Solution Approach 2:
The patent implements a hybrid architecture that applies sinusoidal activations selectively to specific layers and neurons rather than uniformly across the entire network. By applying periodic activations only where necessary for capturing temporal patterns, the model achieves high-frequency detail capture with reduced computational overhead compared to full SIREN implementation
3Adaptability or versatility
If complex modulated auto-decoding framework is used to handle irregular sampling, then generalization capability improves, but device complexity increases
Solution Approach 1:
The patent designs a unified implicit neural representation framework that simultaneously handles irregular sampling, variable input lengths, and multivariate time series within a single model architecture. The sinusoidal-based INR serves multiple functions: continuous representation, temporal pattern capture, and interpolation, eliminating the need for separate modules for each function and reducing overall system complexity
Solution Approach 2:
The patent extracts and removes discrete time step dependencies from the model architecture, replacing them with continuous implicit representations. By eliminating the need for sequential processing of discrete timesteps and using continuous sinusoidal functions instead, the model handles irregular sampling naturally without requiring complex attention mechanisms or recurrent structures
Data Source
AI summary
A method and a system for performing multivariate time series imputation by using a modulated auto-decoding framework that is built upon implicit neural representations are provided. The method includes: receiving information that relates to a latent vector representation of a time series for which there are missing values; using the received information to generate a set of network weights that is usable by a sinusoidal representation network model for obtaining a functional representation of the time series; modulating a set of sine activation amplitudes of the functional representation of the time series; and using the network weights and the modulated sine activation amplitudes to impute the missing values. The method is performable by using three different neural network models for various functions.