Time-Series Model Influence Factor Detection for Decision-Making
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Solution Overview
Problem
Existing causal analysis methods for decision-making in fields like retail, energy control, and abnormal event intervention rely heavily on manual setting, leading to high labor costs and inaccurate results due to reliance on human effort, making it difficult to achieve optimal decisions.
Innovation Solution
A data processing method utilizing a time-series model to determine influence factors affecting a target attribute parameter, automating the decision-making process and improving efficiency and accuracy by leveraging a time-series dataset and model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual setting of causal factors is used, then decision-making can be performed, but labor costs increase and accuracy decreases
Solution Approach 1:
The system performs self-service by automatically identifying causal factors and their relationships through data-driven analysis. The causal analysis module autonomously processes time-series data to determine influence factors without requiring manual specification, thereby eliminating labor costs while maintaining high accuracy through objective computational methods.
Solution Approach 2:
The patent replaces the manual mechanical process of setting causal factors with an automated computational system. The causal analysis module uses algorithms to automatically identify and quantify relationships between variables, substituting human effort with machine-based data processing that is both faster and more accurate.
2Reliability
If manual setting of causal factors is used, then decision-making can be performed, but reliability decreases due to human errors
Solution Approach 1:
The system achieves self-service by automatically performing causal factor identification and relationship determination. This eliminates human errors inherent in manual setting while maintaining system reliability through consistent, reproducible computational analysis that can be validated and audited.
Solution Approach 2:
The causal analysis module incorporates feedback mechanisms where the results of automated analysis are continuously validated against actual outcomes. This feedback loop allows the system to refine its causal models and improve reliability over time, while the automated nature prevents human error from compromising results.
Data Source
AI summary
Embodiments of the present disclosure provide a data processing method and an electronic device, which relate to the computer field. The method includes: acquiring a time-series dataset, the time-series dataset comprising multiple time-series data items each comprising a time and multiple attribute parameters corresponding thereto; acquiring a target attribute parameter being at least one of the multiple attribute parameters; determining, based on a time-series model, at least one influence factor of the target attribute parameter, the at least one influence factor indicating at least one attribute parameter influencing the target attribute parameter and at least one time corresponding to the at least one attribute parameter; and outputting the at least one influence factor. As such, according to the embodiments of the present disclosure, for a target attribute parameter, at least one influence factor can be determined based on a time-series dataset and a time-series model. Therefore, an accurate reference can be provided for a decision on the target attribute parameter, and the decision made in this way is more accurate.