Latent State-Space Forecasting for Non-Stationary Time Series
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Solution Overview
Problem
Existing time series forecasting methods, particularly those using deep learning models, struggle with capturing latent causal dynamics due to stringent assumptions that do not hold in practice, leading to sub-optimal results and spurious dependencies, especially in non-stationary environments.
Innovation Solution
A non-parametric state space model (NPSSM) with a flexible emission model and a hierarchical VAE framework is employed to learn time-varying latent processes, incorporating identifiability conditions for accurate forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If state space models with stringent assumptions are used, then the model structure is simplified, but the forecasting accuracy deteriorates due to inability to approximate actual data generation process
Solution Approach 1:
The patent transforms the fixed, stringent assumptions of traditional state space models into flexible, learnable parameters. The transition function f and emission function g are parameterized as neural networks with learnable weights, allowing the model to adapt to actual data generation processes while maintaining the state space framework structure.
Solution Approach 2:
The patent introduces time-varying parameters that allow the model to dynamically adapt to changing data distributions. The transition and emission functions can change over time, enabling the model to capture non-stationary patterns in the data while preserving the underlying state space structure.
2Adaptability or versatility
If deep learning models are used to capture latent causal dynamics, then the modeling flexibility is improved, but the reliability deteriorates due to capture of spurious dependencies
Solution Approach 1:
The patent segments the complex causal relationships into distinct latent variables representing different causal factors. Each latent variable captures specific causal dynamics, allowing the model to learn flexible representations while maintaining interpretability and reducing spurious dependencies through structured decomposition.
Solution Approach 2:
The patent introduces latent variables as intermediary representations between observed data and predictions. These latent variables serve as mediators that capture causal dynamics in a structured manner, filtering out spurious dependencies while preserving true causal relationships through the hierarchical VAE framework.
3Productivity
If traditional state space models are used, then the computational efficiency is maintained, but the ability to handle non-stationary environments deteriorates
Solution Approach 1:
The patent extends traditional state space models by making the transition and emission functions dynamic and time-varying. This allows the model to adapt to non-stationary environments where data distributions change over time, while still maintaining the computational efficiency of the state space framework through structured probabilistic modeling.
Solution Approach 2:
The patent allows model parameters to change over time through the neural network parameterization. The transition function f(s_t, a_t; θ) and emission function g(s_t; φ) have learnable parameters that can adapt to changing environments, enabling the model to handle non-stationarity while preserving computational tractability.
Data Source
AI summary
Systems and methods for providing a neural network system for time series forecasting are described. A time series dataset that includes datapoints at a plurality of timestamps in an observed space is received. The neural network system is trained using the time series dataset. The training the neural network includes: generating, using an encoder of the neural network system, one or more estimated latent variables of a latent space for the time series dataset; generating, using an auxiliary predictor of the neural network system, a first latent-space prediction result based on the one or more estimated latent variables; transforming, using a decoder of the neural network system, the first latent-space prediction result to a first observed-space prediction result; and updating parameters of the neural network system based on a loss based on the first observed-space prediction result.