Data Sequence Prediction Using Clustering for Object Classification
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Solution Overview
Problem
Existing data sequence prediction methods require manual definition of prediction object classes, which is inefficient and inaccurate, especially when dealing with a large quantity of products.
Innovation Solution
A data sequence prediction method that automatically defines prediction object classes using a clustering algorithm, calculating similarity distances between objects based on historical data sequences and dividing them into prediction object classes for accurate future data sequence prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual definition of prediction object classes is used, then prediction consistency can be ensured, but efficiency and accuracy deteriorate when dealing with large quantities of products
Solution Approach 1:
The system performs self-service by automatically defining prediction object classes through clustering algorithms without requiring manual intervention. The algorithm autonomously analyzes historical data sequences, calculates similarity distances, and groups products into prediction object classes, eliminating the inefficiency of manual classification while maintaining prediction consistency
Solution Approach 2:
The patent replaces the mechanical manual classification process with an automated computational system. Instead of human operators manually defining prediction object classes, the system uses clustering algorithms that compute similarity distances between products based on historical data sequences, automatically generating prediction object classes with higher efficiency and accuracy
2Reliability
If manual definition of prediction object classes is used, then prediction consistency can be maintained, but time consumption increases
Solution Approach 1:
The system performs preliminary action by pre-processing historical data sequences and pre-calculating similarity distances between products. This preliminary computation enables the clustering algorithm to quickly and automatically define prediction object classes without time-consuming manual intervention, while ensuring prediction consistency through systematic data-driven classification
Solution Approach 2:
The automated system serves itself by independently completing the entire classification process from data input to prediction object class definition. The system automatically calculates similarity metrics, applies clustering algorithms, and generates prediction object classes without requiring human time investment, thereby maintaining reliability while reducing time loss
Data Source
AI summary
A data sequence prediction method includes calculating, based on historical data sequences of N objects, a similarity distance between every two objects of the N objects, to obtain a similarity distance set, where the similarity distance is used to represent a similarity degree of two objects, the historical data sequence includes a plurality of pieces of data arranged according to a preset rule, and N is a positive integer greater than 1, dividing the N objects into K prediction object classes based on the similarity distance set using a clustering algorithm, where K is a positive integer, and K≤N, and predicting a future data sequence of an object included in at least one prediction object class of the K prediction object classes.