Variable Seasonality Forecasting via Dynamic Period Selection
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Solution Overview
Problem
Current machine-learning models for seasonal pattern detection and forecasting, such as the Holt-Winters model, are limited in handling variable seasonality and complex patterns, leading to inaccurate forecasts and inefficient resource utilization due to their assumption of fixed seasonal periods and inability to detect irregularities.
Innovation Solution
The approach involves converting time-series data to an outlier space to identify long-term seasonal patterns, using encoding spaces to map timestamps to specific seasonal encodings, and training models based on detected patterns, allowing for flexible and scalable detection of multiple seasonal types without the need for separate models for each type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If Holt-Winters model with fixed seasonal period is used, then model simplicity is maintained, but accuracy in handling variable seasonality deteriorates
Solution Approach 1:
The patent implements dynamic seasonal period detection by training multiple Holt-Winters models with different seasonal periods (e.g., 7, 14, 21, 28 days) and automatically selecting the best-fitting model based on validation performance. This allows the system to adapt to variable seasonality patterns without manually specifying a fixed period, resolving the contradiction between model simplicity and forecasting accuracy for variable seasonal data.
Solution Approach 2:
The system changes the seasonal period parameter dynamically by testing multiple candidate values (7, 14, 21, 28 days) and selecting the optimal parameter based on forecast accuracy metrics. This parameter variation approach enables the model to handle variable seasonality while maintaining the simplicity of the Holt-Winters framework, addressing the contradiction between fixed parameter constraints and adaptive performance.
2Measurement precision
If multiple separate models are trained for each seasonal type, then detection accuracy for specific patterns is improved, but computational overhead increases
Solution Approach 1:
The patent merges multiple seasonal detection approaches by implementing a unified system that tests multiple seasonal periods within a single forecasting framework. Instead of maintaining completely separate models for each pattern type, the system combines them into one adaptive process that selects the appropriate seasonal period based on data characteristics, reducing computational overhead while maintaining detection accuracy.
Solution Approach 2:
The system segments the seasonal period space into discrete candidate values (7, 14, 21, 28 days) and evaluates each segment independently through cross-validation. This segmentation allows efficient comparison of different seasonal patterns without requiring continuous parameter optimization, balancing computational efficiency with accurate pattern detection.
3Productivity
If fixed seasonal period assumption is made, then model training speed is maintained, but adaptability to variable seasonality deteriorates
Solution Approach 1:
The system performs preliminary action by pre-defining a discrete set of candidate seasonal periods (7, 14, 21, 28 days) before training begins. This preliminary preparation allows the model to quickly evaluate only these specific periods through cross-validation rather than searching continuous parameter spaces, maintaining training speed while improving adaptability to variable seasonality patterns.
Solution Approach 2:
The system implements dynamics by automatically adjusting the seasonal period parameter based on validation performance across different candidate values. Rather than fixing the period beforehand, the model dynamically selects the optimal seasonal period from multiple candidates, enabling adaptation to variable seasonality while keeping the training process efficient through the use of discrete candidate sets.
Data Source
AI summary
Techniques for training and evaluating seasonal forecasting models are disclosed. In some embodiments, a network service generates, in memory, a set of data structures that separate sample values by season type and season space. The set of data structures may include a first set of clusters corresponding to different season types in the first season space and a second set of clusters corresponding to different season types in the second season space. The network service merges two or more clusters the first set and/or second set of clusters. Clusters from the first set are not merged with clusters from the second set. After merging the clusters, the network service determines a trend pattern for each of the remaining clusters in the first and second set of clusters. The network service then generates a forecast for a metric of a computing resource based on the trend patterns for each remaining cluster.