Genome-Layer Transformer for Low-Data Time Series Forecasting
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Solution Overview
Problem
Conventional time series forecasting methods using transformers and neural networks face challenges such as lack of accuracy, requirement of extensive training data and time, high memory complexity, inability to capture non-linear dependencies and seasonality patterns, and reliance on static thresholds leading to false positives or missed anomalies, which are exacerbated by the dynamic and complex nature of data in IT operations.
Innovation Solution
A modified transformer model with a genome sequence layer that dynamically adjusts neural network connections and configurations based on environment data, eliminating the need for extensive training data and manual setup, and incorporating dynamic thresholding for anomaly detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional transformers and neural networks are used for time series forecasting, then forecasting capability is provided, but accuracy is insufficient and extensive training data is required
Solution Approach 1:
The patent applies dynamics by replacing static neural network architectures with a genome sequence-based system that dynamically evolves and adapts to different time series data characteristics. The genome sequence allows the model to automatically adjust its structure and parameters based on the specific requirements of each forecasting task, eliminating the need for extensive training data while improving accuracy.
Solution Approach 2:
The patent utilizes parameter changes by encoding neural network configurations in the genome sequence, allowing the model to transform and adapt its parameters based on the input data characteristics. This enables the system to optimize its forecasting performance for different time series patterns without requiring retraining on extensive data, thus improving accuracy while reducing data requirements.
2Productivity
If conventional neural networks are used for time series forecasting, then forecasting is performed, but extensive training time is required
Solution Approach 1:
The patent applies preliminary action by pre-defining the neural network architecture and parameters in the genome sequence before actual forecasting. This allows the model to be ready for immediate use without requiring extensive training time, as the genome sequence already encodes the optimized configuration needed for efficient forecasting operations.
Solution Approach 2:
The system dynamically loads and executes the genome sequence to configure the neural network for each forecasting task, eliminating the need for time-consuming training processes. This dynamic approach allows the model to adapt to different time series data in real-time without requiring extensive training time, significantly improving forecasting speed.
3Measurement precision
If conventional transformer models are used, then text processing capability is provided, but they lack accuracy for time series forecasting tasks
Solution Approach 1:
The patent applies local quality by customizing the model's behavior for specific time series forecasting tasks through the genome sequence. Instead of using a generic transformer architecture, the system locally adapts the neural network configuration to match the specific characteristics of time series data, improving accuracy while maintaining versatility across different forecasting scenarios.
Solution Approach 2:
The system changes parameters by encoding task-specific configurations in the genome sequence, allowing the model to adapt its architecture and parameters to the specific requirements of time series forecasting. This enables the system to achieve high accuracy for time series tasks while maintaining the versatility to handle different data types and patterns.
4Reliability
If static thresholds are used for anomaly detection, then simple detection is achieved, but false positives and missed anomalies occur due to inability to adapt to changing conditions
Solution Approach 1:
The patent applies dynamics by replacing static anomaly detection thresholds with a genome sequence-based system that dynamically adapts to changing data conditions. The genome sequence enables the model to automatically adjust detection parameters based on the specific characteristics of the time series data, improving reliability while managing complexity through automated adaptation rather than manual configuration.
Solution Approach 2:
The system incorporates feedback mechanisms where the genome sequence continuously learns from and adapts to the detected anomalies and data patterns. This feedback loop allows the anomaly detection system to improve its accuracy over time by adjusting its thresholds and parameters based on actual performance, reducing false positives and missed anomalies.
5Measurement precision
If conventional forecasting models are used, then basic forecasting is provided, but they cannot capture non-linear dependencies and seasonality patterns
Solution Approach 1:
The patent utilizes parameter changes by encoding complex non-linear relationships and seasonality patterns directly in the genome sequence. This allows the model to capture intricate temporal dependencies and seasonal variations without requiring overly complex architecture, as the genome sequence inherently encodes the necessary patterns and relationships for accurate forecasting.
Solution Approach 2:
The system applies preliminary action by pre-encoding the recognition of non-linear dependencies and seasonality patterns in the genome sequence before actual forecasting. This allows the model to immediately identify and capture these complex patterns when processing time series data, improving pattern recognition accuracy without requiring complex real-time computation.
Data Source
AI summary
Disclosed below is a system and method for time series forecasting using a modified transformer. The modified transformer comprises of a genome sequence embedded which replaces the decoder in a traditional transformer for time series forecasting. Further, the disclosed system and method can also be used for an accurate and reliable prediction and forecasting, which also drastically reduces the processor requirements and the time required. The core of the invention is the genome layer which is present in the transformer for time series forecasting, which determines the connections and configuration of the neural network.