Variance-Based Time Series Dataset Alignment
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Solution Overview
Problem
Time-series datasets from different sources in computer networks often lack temporal alignment due to varying aggregation intervals, offsets, and missing data points, making it difficult to analyze and diagnose performance issues effectively.
Innovation Solution
A system that automatically aligns time-series datasets by evaluating their variance, selecting appropriate alignment processes based on smoothness thresholds, and interpolating or filtering data to generate aligned datasets with common time stamps, enabling correlation analysis and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If time-series datasets from different sources are collected with varying aggregation intervals and offsets, then the quantity and diversity of data increases, but temporal alignment between datasets deteriorates
Solution Approach 1:
The system changes the time parameter by calculating variance at different time offsets and selecting the offset that minimizes variance, thereby achieving temporal alignment between datasets with different aggregation intervals
Solution Approach 2:
The patent replaces manual temporal alignment processes with an automated statistical method using variance calculation and optimization, eliminating the need for manual synchronization of datasets from different sources
2Measurement precision
If multiple alignment processes are used to handle different data smoothness cases, then alignment accuracy improves, but system complexity increases
Solution Approach 1:
The system applies different alignment strategies based on the local characteristic of data smoothness, using linear interpolation for smooth datasets and nearest-neighbor interpolation for noisy datasets, thereby optimizing alignment accuracy for each specific case
Solution Approach 2:
The alignment process dynamically adapts its method based on the calculated variance of the data, automatically selecting between different interpolation approaches according to the actual data characteristics rather than using a fixed method
3Measurement precision
If automated variance-based alignment is implemented, then alignment accuracy improves, but computational time increases
Solution Approach 1:
The system performs a limited search over possible time offsets rather than exhaustively evaluating all possible alignments, achieving sufficient alignment accuracy with reduced computational effort by focusing on the most likely offset ranges
Data Source
AI summary
An example process for aligning time-series datasets includes receiving a first time-series dataset and a second time-series dataset. The first time-series dataset can include a first set of values associated with respective time stamps and the second time-series dataset can include a second set of values associated with respective time stamps. The process also includes determining degrees of variance of the first and second sets of values, and comparing each degree of variance with a threshold. The process also includes selecting among multiple time alignment processes based on the comparisons, and processing the time-series datasets according to the selected process to thereby generate an aligned time-series dataset.