Neural Network Component Definition Layers for Time Series Insight
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Solution Overview
Problem
Existing time series models struggle to integrate subject matter expert (SME) knowledge effectively, often failing to identify the most contributing components to model decisions due to qualitative nature and lack of guidance in model selection and component arrangement.
Innovation Solution
A method and system that translate SME input into a model template using a rule-based translator, generating a machine learning model as a multilayer neural network with component definition layers to extract and quantify the contribution of each component to decisions, enabling automated insight derivation from time series data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing time series models are used, then model selection and component arrangement can be performed, but SME knowledge cannot be integrated effectively due to qualitative nature and lack of guidance
Solution Approach 1:
A rule-based translator is introduced as an intermediary component that converts qualitative SME knowledge into quantitative model parameters. The translator acts as a mediator between human expertise and machine learning models, systematically transforming textual descriptions into structured model templates that can be automatically processed by neural networks.
Solution Approach 2:
The model is segmented into multiple component definition layers, each responsible for extracting specific time series components (trend, seasonality, cyclicity, noise). This segmentation allows systematic integration of SME knowledge at different levels of the model hierarchy, with each layer handling specific aspect of time series decomposition.
2Loss of information
If traditional time series analysis is used, then statistical relatedness can be determined, but component-wise contribution to decisions cannot be identified
Solution Approach 1:
The neural network is divided into multiple component definition layers, with each layer extracting and representing specific time series components separately. This segmentation enables the model to track and quantify the contribution of each component (trend, seasonality, cyclicity, noise) to final predictions, providing interpretability while maintaining computational efficiency.
3Productivity
If automated model generation is implemented, then productivity can be improved, but model accuracy may suffer due to lack of expert guidance
Solution Approach 1:
SME knowledge is captured and translated into model templates before the actual model training begins. This preliminary action ensures that expert guidance is embedded in the model structure and component definitions before automated generation occurs, maintaining accuracy while enabling rapid automated deployment.
Solution Approach 2:
The rule-based translator serves as an intermediary that preserves the precision of expert knowledge while enabling automated model generation. It translates qualitative SME input into quantitative parameters that maintain analytical rigor, allowing automated processes to produce accurate models without sacrificing reliability.
Data Source
AI summary
Deriving insights from time series data can include receiving subject matter expert (SME) input characterizing one or more aspects of a time series. A model template that specifies one or more components of the time series can be generated by translating the SME input using a rule-based translator. A machine learning model based on the model template can be a multilayer neural network having one or more component definition layers, each configured to extract one of the one or more components from time series data input corresponding to an instantiation of the time series. With respect to a decision generated by the machine learning model based on the time series data input, a component-wise contribution of each of the one or more components to the decision can be determined. An output can be generated, the output including the component-wise contribution of at least one of the one or more components.