Chart-Based Time Series Regression UI for Rapid Model Iteration
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Solution Overview
Problem
Existing systems face challenges in quickly producing models for time series data sets due to the iterative nature of modeling and the lack of insight into the data being used, making it difficult to select appropriate model inputs and requiring time-consuming code changes, especially when model inputs change rapidly.
Innovation Solution
A user interface that facilitates rapid selection and visualization of model inputs, allowing users to interact with time series data through an ontology-based system, enabling real-time display of model outputs and allowing for iterative refinement using features from different batches or systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional modeling approaches are used, then model reliability is improved, but productivity deteriorates due to time-consuming code changes and iterative processes
Solution Approach 1:
The system creates visual copies of time series data as selectable chart elements that represent model inputs. Users can copy data patterns visually and apply them to model training without writing code, enabling rapid experimentation with different data configurations while maintaining reliable modeling outcomes.
Solution Approach 2:
The patent introduces an intermediary visualization layer between raw time series data and model training. This intermediate chart-based interface allows users to inspect, select, and configure data inputs visually before they reach the modeling process, eliminating the need for direct code manipulation while preserving model reliability.
2Measurement precision
If manual code changes are made for iterative modeling, then measurement precision is improved, but loss of time increases due to repeated code modification and retraining
Solution Approach 1:
The system implements dynamic, interactive charts where users can continuously adjust data selection, time ranges, and feature configurations during the modeling process. This dynamic interface allows rapid iteration without code changes, maintaining precise data selection while dramatically reducing the time required for each iteration cycle.
Solution Approach 2:
The system performs preliminary data processing, validation, and visualization before model training begins. By preparing and displaying data characteristics in advance through interactive charts, users can make informed selection decisions without needing to write or modify code for each data exploration step.
3Object-generated harmful factors
If comprehensive data inspection is performed, then object-generated harmful factors are reduced, but device complexity increases due to additional data processing requirements
Solution Approach 1:
The system provides self-service data inspection and filtering capabilities through automated chart generation and interactive exploration tools. The visualization system automatically processes and displays data characteristics, allowing users to identify and filter noise without requiring complex manual processing pipelines or additional system infrastructure.
Data Source
AI summary
Methods and systems for providing a user interface and workflow for interacting with time series data, and applying portions of time series data sets for refining regression models. A system can present a user interface for receiving a first user input selecting a first model from a list of models for modeling the apparatus, generate and display a first chart depicting a first time series data set depicting data from a first sensor, generate and display a second chart depicting a second time series data set depicting a target output of the apparatus, receive a second user input of a portion of the first time series data set, and generate and display a third chart depicting a third time series data set depicting an output of the selected model and aligned with the second chart of the target output and updated in real-time in response to the second user input.