DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios

DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios

Accurate long-term cloud demand forecasting is critical for optimizing resource procurement and cost management in cloud computing, yet it remains challenging due to dynamic demand trends, limited historical data, and the poor generalization of existing models in few-shot scenarios. This paper propo...

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Bibliographic Details
Main Authors: Xiaoyi Yang, Qiming Zheng, Xiaoyu Zhu, Mianzhang Luo, Zheng Hou, Jiatai Zhang, Yuqing Lan
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Applied Sciences
Subjects:
time series prediction
data augmentation
finops
cloud demand forecasting
large time series models
Online Access:https://www.mdpi.com/2076-3417/15/7/3450
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Summary:Accurate long-term cloud demand forecasting is critical for optimizing resource procurement and cost management in cloud computing, yet it remains challenging due to dynamic demand trends, limited historical data, and the poor generalization of existing models in few-shot scenarios. This paper proposes DimAug-TimesFM, a dimension-augmented framework for long-term cloud demand forecasting, which addresses these challenges through two key innovations. First, Delivery Period Extracting identifies critical resource delivery phases by analyzing smoothed utilization trends and differencing thresholds, enabling focused modeling on periods reflecting actual demand. Second, Dimension-Augmented TimesFM enhances the pretrained TimesFM model by integrating cross-pool data via Dynamic Time Warping based similarity matching, enriching training data while mitigating distribution discrepancies. Experiments on real-world cloud resource utilization data demonstrate that DimAug-TimesFM significantly outperforms SOTA baselines (e.g., TimesFM, DLinear, PatchTST) in both short-term (16-day) and long-term (64-day and 128-day) forecasting tasks, achieving average reductions 72.9–81.7% in RMSE. DimAug-TimesFM also exhibits better robustness in scenarios where TimesFM fails, attributed to its synergistic integration of temporal feature enhancement and cross-pool data augmentation. This work provides a practical solution for few-shot cloud demand forecasting, enabling enterprises to align resource allocation with dynamic usage patterns and reduce operational costs.
ISSN:2076-3417

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