Time Series Analysis for Online Network Performance Metrics
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Solution Overview
Problem
Online professional networks face challenges in determining how user actions influence performance metrics, such as page views and unique users, due to the complexity of analyzing these relationships over time.
Innovation Solution
A system utilizing time series analysis techniques, specifically Seasonal Vector AutoRegressive Integrated Moving Average (SVARIMAX) models with exogenous variables, is employed to construct and solve models that determine relationships between user actions and performance metrics, incorporating multivariate regression and variable selection methods like LASSO to handle non-stationarity and seasonal patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If time series analysis techniques are used to determine relationships between user actions and performance metrics, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex analysis task into distinct components: data collection from multiple sources, time series construction, model selection from different categories (ARIMA, VAR, SVAR, VARIMA), and incremental updating. This segmentation allows the system to handle complexity through modular processing while maintaining high measurement precision in determining relationships between user actions and performance metrics.
2Measurement precision
If sophisticated analysis tools are used to determine how user actions influence performance metrics, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent implements dynamic model selection and incremental updating capabilities. The system can adaptively choose from multiple model categories (ARIMA for single metric, VAR for multiple metrics, SVAR for causal relationships, VARIMA for seasonal patterns) and update models incrementally as new data arrives. This dynamic approach maintains high measurement precision while managing complexity through flexible, adaptive processing rather than static complex structures.
3Reliability
If time series analysis is performed to identify relationships between user actions and performance metrics, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent incorporates feedback mechanisms through incremental model updating and validation. The system continuously refines its understanding of relationships between user actions and performance metrics by processing new data and updating models incrementally. This feedback loop improves reliability by ensuring models remain accurate and current, while managing complexity through iterative refinement rather than requiring overly complex static structures.
Data Source
AI summary
The disclosed embodiments relate to a system for analyzing performance in an online professional network. During operation, the system receives time series data for user actions, wherein for each user action, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates a number of times the user action occurred during the time interval. The system also receives time series data for performance metrics, wherein for each performance metric, the time series data comprises a series of numbers associated with consecutive time intervals, wherein a given number indicates the number of times the performance metric occurred during the time interval. The system then performs a time series analysis on the received time series data for user actions and performance metrics to determine relationships between the user actions and the performance metrics.