Multivariate Time Series Forecasting via Latent Space Encoding
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Solution Overview
Problem
Current methods for multivariate time series modeling and forecasting struggle to capture cross-series effects, non-linear relationships, and incorporate exogenous information, leading to poor decision-making and inefficiencies due to their lack of scalability and inability to handle complex data.
Innovation Solution
A computing device employing a neural network-based deep learning model for multivariate time series modeling, which encodes input data into a lower-dimensional latent space, predicts next values, and maps them back to the input space with random noise to generate predictive distributions, improving accuracy and efficiency through unsupervised training and stochastic gradient descent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual heuristic approaches are used for time series modeling, then the model can be easily understood and implemented, but it lacks the flexibility to capture cross-series effects and is not scalable to large numbers of time series
Solution Approach 1:
The patent segments the time series data by decomposing it into multiple components (trend, seasonality, residuals) and processes each component separately through neural networks. This allows the model to capture complex cross-series effects while maintaining manageable complexity through modular processing of individual components.
Solution Approach 2:
The patent introduces latent variables as intermediaries that capture cross-series effects. These latent variables serve as mediators between input time series data and predictions, enabling the model to capture underlying relationships without directly modeling all pairwise interactions between series.
2Measurement precision
If traditional linear models are used for time series forecasting, then the model is computationally efficient and easy to interpret, but it cannot capture underlying non-linear relationships and effects across time series
Solution Approach 1:
The patent replaces traditional mechanical linear modeling approaches with neural network-based systems that automatically learn non-linear relationships. The neural networks substitute for manual feature engineering and linear assumptions, capturing complex non-linear patterns while maintaining computational efficiency through optimized training procedures.
Solution Approach 2:
The patent transforms the time series data through non-linear parameter changes using neural network activations and transformations. By applying non-linear transformations to input features and learned representations, the model captures complex relationships that linear models cannot detect, while the parameter transformations are computed efficiently during training.
3Measurement precision
If exogenous information is incorporated into time series models, then the forecasting accuracy improves by accounting for external factors, but the model complexity increases and processing requirements increase
Solution Approach 1:
The patent merges exogenous information with endogenous time series data by concatenating them as joint inputs to the neural network. This combining approach allows the model to process both types of information uniformly through the same architecture, improving forecasting accuracy while avoiding the need for separate processing pipelines that would increase complexity.
Solution Approach 2:
The patent designs a universal neural network architecture that handles both endogenous time series data and exogenous information through the same processing pathways. This multi-functional approach allows the model to process different types of inputs uniformly, improving accuracy from exogenous factors while maintaining consistent model complexity regardless of input type.
4Measurement precision
If deep learning models with multiple layers are used for time series modeling, then the model can capture complex patterns and relationships, but the training time and computational resources increase significantly
Solution Approach 1:
The patent performs preliminary data processing and feature extraction before feeding data to the deep learning model. By pre-processing the time series data to extract relevant features and reduce dimensionality beforehand, the model requires fewer training iterations to learn complex patterns, reducing overall training time while maintaining pattern recognition capability.
Solution Approach 2:
The patent employs dynamic training strategies including adaptive learning rates and early stopping based on validation performance. These dynamic adjustments optimize the training process by increasing learning rate when progress is good and reducing it when convergence is approaching, significantly reducing training time while ensuring the model captures complex patterns effectively.
Data Source
AI summary
A computing device for time series modeling and forecasting includes a processor, and a memory coupled to the processor. The memory stores instructions to cause the processor to perform acts including encoding an input of a multivariate time series data, and performing a non-linear mapping of the encoded multivariate time series data to a lower-dimensional latent space. The next values in time of the encoded multivariate time series data in the lower dimensional latent space are predicted. The predicted next values and a random noise are mapped back to an input space to provide a predictive distribution sample for a next time points of the multivariate time series data. One or more time series forecasts based on the predictive distribution sample are output.