Unified Latent Variable Framework for Multi-Type Time Series Clustering
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Solution Overview
Problem
Conventional time series modeling approaches are data type specific, limiting their ability to generalize across different data types for clustering and anomaly detection tasks.
Innovation Solution
An information processing apparatus and method that acquires multiple data streams, recursively assigns clusters, updates dynamics and latent states, and transforms latent states into observation model parameters using type-specific transformation functions, enabling a general framework for time series modeling across various data types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a data type specific clustering algorithm is used, then the algorithm can be optimized for that specific data type, but it cannot generalize to other data types
Solution Approach 1:
The patent applies universality by designing a single unified framework that can handle multiple data types (continuous, discrete, categorical, trajectory, count data) through a common probabilistic latent variable model. The observation model is made adaptable to different data types through type-specific probability distributions while maintaining a consistent overall architecture, allowing the same framework to achieve good clustering accuracy across diverse data types without requiring separate algorithms for each type.
2Measurement precision
If separate models are constructed for each data type, then each model can be optimized for its specific data type, but the overall system complexity increases
Solution Approach 1:
The patent merges multiple data type-specific models into a single unified probabilistic framework. Instead of maintaining separate clustering algorithms for continuous, discrete, categorical, trajectory, and count data, the invention combines them all under one generative model with a unified latent variable structure and observation model. This consolidation reduces system complexity by eliminating the need to manage multiple separate models while still allowing optimization for each data type through appropriate probability distributions.
3Adaptability or versatility
If a general framework is created to accommodate different data types, then adaptability improves, but the complexity of the observation model increases
Solution Approach 1:
The patent applies local quality by making the observation model adaptable to different data types through localized modifications. The core probabilistic latent variable model remains consistent across all data types, but the observation model uses type-specific probability distributions (Gaussian for continuous, Poisson for count data, categorical distributions for categorical data, etc.). This allows the system to accommodate different data types effectively while keeping the increase in complexity localized to the observation model layer rather than propagating through the entire system.
Data Source
AI summary
The information processing apparatus (2000) of the example embodiment 1 includes an acquisition unit (2020), a clustering unit (2040), a transformation unit (2060) and modeling unit (2080). Until a predetermined termination condition is determined, the clustering unit (2040) repeatedly preforms: 1) optimizing the posterior parameters for clustering assignment for each data streams; 2) optimizes the posterior parameters for each determined cluster and for each time frame; 3) optimizes the posterior parameters for individual responses for each data stream; 4) optimizes the posterior parameters for latent states, via approximating the observation model through non-conjugate inference. The transformation unit (2060) transforms the latent states into parameters of the observation model, through a transformation function. The modeling unit (2060) generates the model data, which including all the optimized parameters of all the model latent variables, optimized inside the clustering unit (2040).