Seasonal Pattern Detection in Time-Series Data
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Solution Overview
Problem
Existing methods for analyzing seasonality in time-series data, such as Holt-Winters models, are limited in detecting multiple seasonal patterns and providing meaningful interpretations of seasonal indices, leading to inaccurate forecasts and inefficient resource utilization due to reliance on single seasonal cycles and internal structures not exposed to end users.
Innovation Solution
A system and method for automatically detecting and generating multiple seasonal patterns within time-series data, using clustering logic, signature generation, and pattern processing to identify and validate seasonal behavior, allowing for capacity planning and anomaly detection without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Holt-Winters models are used to analyze seasonality, then seasonal patterns can be identified, but the system can only detect single seasonal patterns and cannot provide meaningful interpretations of seasonal indices
Solution Approach 1:
The patent segments the time-series data by identifying multiple distinct seasonal patterns instead of assuming a single seasonal cycle. The system divides the data into different seasonal components, each with its own characteristics, allowing simultaneous detection of multiple seasonalities (e.g., daily, weekly, and monthly patterns) without interference between them.
Solution Approach 2:
The patent adds a new dimension to seasonal analysis by introducing pattern strength metrics and cluster-based classification. Instead of merely identifying one seasonal period, the system evaluates seasonal patterns across multiple dimensions including strength, duration, and significance, enabling meaningful interpretation of seasonal indices through statistical clustering and validation.
2Productivity
If traditional seasonal analysis methods are used, then forecasting can be performed, but resource allocation remains inefficient due to lack of actionable insights
Solution Approach 1:
The patent implements feedback mechanisms by continuously monitoring seasonal pattern strength and validity. The system provides actionable insights through pattern validation metrics and confidence scores that feed into resource allocation decisions. This feedback loop enables dynamic adjustment of resources based on validated seasonal patterns rather than static assumptions.
Solution Approach 2:
The system performs self-validation of seasonal patterns through automated statistical testing and cluster analysis. Instead of requiring manual intervention to verify seasonal assumptions, the system autonomously validates patterns, generates confidence metrics, and provides ready-to-use insights for resource allocation, making the forecasting process self-sufficient and immediately actionable.
3Loss of time
If seasonal patterns are assumed without validation, then analysis can proceed quickly, but accuracy suffers due to unvalidated assumptions
Solution Approach 1:
The patent performs preliminary validation of seasonal patterns before proceeding with full analysis. The system conducts initial statistical tests and cluster validations to confirm the presence and strength of seasonal patterns before committing to detailed forecasting. This preliminary action ensures that only validated patterns are used, maintaining both speed and accuracy.
Solution Approach 2:
The patent replaces manual assumption-making with automated statistical validation mechanisms. Instead of relying on human experts to assume seasonal patterns based on experience, the system uses objective statistical tests, cluster analysis, and validity metrics to automatically verify patterns, substituting mechanical validation processes for subjective assumptions.
Data Source
AI summary
Techniques are described herein for seasonal pattern determination and validation. In one or more embodiments, a set of time-series data is received to analyze for seasonal behavior. In response a plurality of patterns may be generated, including a first pattern and a second pattern, such that each of the first pattern and the second pattern approximate data points that represent a same sub-period of multiple instances of a season within the set of time-series data. One or more other instances of the season may then be analyzed to determine whether at least part of the first pattern or the second pattern is detected. Based at least in part on determining that the at least part of the first pattern is detected in the at least part of the same sub-period, a responsive action that is associated with the first pattern may be performed.