Metric Forecasting Analytics System Using Seasonal Factor Extraction
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Solution Overview
Problem
Conventional analytics systems lack the ability to forecast future metrics for service provider systems in digital environments, limiting their ability to plan and predict future operations such as visitor traffic, computational resource consumption, revenue, and expenses.
Innovation Solution
The implementation of metric forecasting techniques in analytics systems, which identify time series intervals in input usage data, select appropriate forecast models based on temporal granularity and availability of historical data, and generate forecast data to predict future metric values, including scheduled periods like holidays, to enhance accuracy and reduce computational resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional analytics systems use historical data to provide insights, then they can analyze past performance and trends, but they cannot predict future metric values
Solution Approach 1:
The system performs preliminary actions by pre-processing historical usage data to identify seasonal patterns and temporal relationships before forecasting is needed. This includes pre-calculating seasonal factors and storing them for rapid retrieval during forecast generation, enabling future predictions without complex real-time computations
Solution Approach 2:
The system creates simplified copies of historical data patterns by generating seasonal factors that represent recurring temporal relationships. These copied patterns are then applied to forecast future metrics without needing to re-analyze complete historical datasets, reducing computational complexity while preserving predictive capability
2Measurement precision
If the analytics system processes complete historical datasets to improve forecast accuracy, then prediction precision increases, but computational resource consumption increases
Solution Approach 1:
The system extracts only the essential seasonal patterns and temporal relationships from complete historical datasets, separating these predictive elements from unnecessary historical detail. By taking out only the relevant seasonal factors needed for forecasting, the system achieves accurate predictions without processing entire historical datasets, thereby reducing computational resource consumption
Solution Approach 2:
The system applies partial action by using only the portion of historical data that contains seasonal patterns relevant to forecasting, rather than processing complete historical datasets. This selective approach focuses computational effort on the most valuable data elements, achieving sufficient forecast accuracy with reduced resource consumption
3Productivity
If the system generates forecasts in real-time using available data, then responsiveness improves, but forecast accuracy may be compromised due to limited historical data
Solution Approach 1:
The system performs preliminary analysis of historical data to pre-calculate seasonal factors and temporal patterns before real-time forecasting is required. This advance preparation stores essential predictive information that can be rapidly applied during real-time operations, enabling both fast forecast generation and accurate predictions even when limited historical data is available
Solution Approach 2:
The system changes parameters by transforming raw historical data into seasonal factor parameters that capture temporal relationships in a compact form. This parameter transformation allows the system to work with reduced data volumes while maintaining forecast accuracy, enabling real-time processing without sacrificing predictive quality
Data Source
AI summary
Metric forecasting techniques in a digital medium environment are described. A time series interval is identified by an analytics system that is exhibited by input usage data. The input usage data describes values of a metric involved in the provision of the digital content by a service provider system. A determination is then made by the analytics system as to whether historical usage data includes the identified time series interval. A forecast model is then selected by the analytics system from a plurality of forecast models based on a result of the determination and the identified time series interval. Forecast data is then generated by a forecast module of the analytics system. The forecast data is configured to predict at least one value of the metric based on the selected forecast model, a result of the determination, and the input usage data.