Neural Network Execution Block for Zero-Shot Time-Series Forecasting
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Deep neural networks face challenges in time-series forecasting due to the 'cold start' problem, requiring large, task-specific datasets for training, which is inefficient and costly, and lack effective transfer learning approaches for out-of-distribution generalization.
Innovation Solution
A neural network system using a novel execution block with a fully connected stack of layers, where parameters are shared across blocks, allowing for zero-shot deployment on new tasks with a generic training dataset, and incorporating waveform generators to constrain waveform possibilities, enabling effective transfer learning and meta-learning for time-series forecasting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks are trained on large task-specific datasets, then forecasting accuracy is improved, but deployment cost and complexity increase
Solution Approach 1:
The system performs preliminary training on a generic dataset before deployment on task-specific data. The neural network is pre-trained on a large generic dataset to learn general temporal patterns and relationships, then fine-tuned on smaller task-specific datasets. This preliminary action reduces the need for extensive task-specific training data and simplifies deployment procedures.
Solution Approach 2:
The neural network architecture is designed with universal components that can handle multiple forecasting tasks. The system uses a generic training dataset that encompasses diverse temporal patterns, enabling the model to generalize across different domains and applications. This multi-functionality allows the same trained model to be applied to various forecasting problems without requiring complete retraining.
2Measurement precision
If deep neural networks are deployed with task-specific training, then model performance is improved, but data collection effort and time increase
Solution Approach 1:
The system performs preliminary training on a generic dataset before deployment on task-specific data. The neural network is pre-trained on a large generic dataset to learn general temporal patterns and relationships, then fine-tuned on smaller task-specific datasets. This preliminary action reduces the need for extensive task-specific training data and simplifies deployment procedures.
Solution Approach 2:
The system uses a generic training dataset as a template or copy that captures universal temporal patterns. This generic dataset serves as a reusable foundation that can be applied across multiple tasks, reducing the need to collect and process large amounts of task-specific training data for each new application.
3Measurement precision
If specialized training datasets are obtained for each task, then task-specific accuracy is improved, but system adaptability decreases
Solution Approach 1:
The neural network architecture is designed with universal components that can handle multiple forecasting tasks. The system uses a generic training dataset that encompasses diverse temporal patterns, enabling the model to generalize across different domains and applications. This multi-functionality allows the same trained model to be applied to various forecasting problems without requiring complete retraining.
Solution Approach 2:
The system performs preliminary training on a generic dataset before deployment on task-specific data. The neural network is pre-trained on a large generic dataset to learn general temporal patterns and relationships, then fine-tuned on smaller task-specific datasets. This preliminary action reduces the need for extensive task-specific training data and simplifies deployment procedures.
4Measurement precision
If extensive retraining is performed for each new task, then model accuracy is improved, but deployment efficiency decreases
Solution Approach 1:
The system performs preliminary training on a generic dataset before deployment on task-specific data. The neural network is pre-trained on a large generic dataset to learn general temporal patterns and relationships, then fine-tuned on smaller task-specific datasets. This preliminary action reduces the need for extensive task-specific training data and simplifies deployment procedures.
Solution Approach 2:
The neural network architecture is designed with universal components that can handle multiple forecasting tasks. The system uses a generic training dataset that encompasses diverse temporal patterns, enabling the model to generalize across different domains and applications. This multi-functionality allows the same trained model to be applied to various forecasting problems without requiring complete retraining.
Data Source
AI summary
Systems, architectures, and approaches for use with neural networks. An execution block and a system architecture using a novel execution block are disclosed along with how such an execution block can be used. The execution block uses a fully connected stack of layers and parameters of this fully connected stack of layers are shared. The fully connected nature of the block and on-the-fly generated parameters allow for bypassing specialized training data sets. The system may be trained using non-task-specific training data sets and this allows the system to transfer what is learned to execute a different task. Thus, instead of having to obtain a specialized training data set for a specific task, a more generic training data set can be used to train and prepare the system for that specific task. Results have shown that performance is as good as than the state of the art in providing solutions.