Convolutional Neural Network for Multi-dimensional Time Series Event Prediction
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Solution Overview
Problem
The vast amount of multi-dimensional time series data generated by various devices poses challenges in management, processing, and analysis, making it difficult for digital technologies to effectively utilize this data for machine learning applications.
Innovation Solution
A system comprising a snapshot component and a machine learning component that generates sequences of multi-dimensional time series data and analyzes them using a convolutional neural network to predict events, with the ability to tune data matrices and neural network parameters based on prediction data for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional machine learning methods are used to analyze multi-dimensional time series data, then the system can process the data, but the processing efficiency and accuracy are insufficient due to the vast amount of data
Solution Approach 1:
The patent segments the multi-dimensional time series data into multiple sequences, where each sequence represents a specific dimension or aspect of the data. This segmentation allows the convolutional neural network to process each sequence independently and efficiently, transforming the overwhelming task of processing vast volumes of data into manageable chunks that can be analyzed in parallel, thereby improving processing efficiency while handling large data volumes.
Solution Approach 2:
The patent replaces traditional mechanical machine learning processing methods with a convolutional neural network system. The CNN automatically learns features and patterns from the segmented time series sequences through hierarchical processing, substituting manual feature engineering and traditional algorithmic approaches with an automated deep learning system that achieves superior processing efficiency and accuracy on large-scale multi-dimensional data.
2Measurement precision
If multi-dimensional time series data from multiple data types is processed, then the prediction accuracy can be improved, but the data management and processing complexity increases
Solution Approach 1:
The patent implements a universal convolutional neural network architecture that can process multiple data types (e.g., numerical, categorical, temporal) through a unified framework. The system applies the same CNN processing pipeline to all data types, automatically adapting to different dimensions and formats. This multi-functional approach enables the system to handle diverse data types simultaneously, improving prediction accuracy through comprehensive analysis while avoiding the complexity of implementing separate processing systems for each data type.
3Measurement precision
If convolutional neural network is used to analyze time series sequences, then the event prediction accuracy is improved, but the computational resources and processing time increase
Solution Approach 1:
The patent applies preliminary segmentation and organization of time series data into structured sequences before feeding them to the convolutional neural network. By pre-processing the data to create well-organized sequences with proper formatting and structure, the system reduces the computational burden during the actual CNN processing phase. This preliminary action enables the network to focus on pattern recognition rather than data organization, improving event prediction accuracy while minimizing additional processing time.
Data Source
AI summary
Techniques that facilitate machine learning using multi-dimensional time series data are provided. In one example, a system includes a snapshot component and a machine learning component. The snapshot component generates a first sequence of multi-dimensional time series data and a second sequence of multi-dimensional time series data from multi-dimensional time series data associated with at least two different data types generated by a data system over a consecutive period of time. The machine learning component that analyzes the first sequence of multi-dimensional time series data and the second sequence of multi-dimensional time series data using a convolutional neural network system to predict an event associated with the multi-dimensional time series data.