# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 1 Introduction  
As enterprises continue to advance their digital transformation initiatives, cloud computing has emerged as an indispensable component of modern business operations. However, managing costs in a cloud environment remains a critical challenge, which is a central focus of FinOps (Financial Operations) [1]. Moreover, the inherent uncertainty of resource demands often hinders enterprises' ability to align their allocations with actual usage, resulting in both over-provisioning and under-utilization. In the context of cloud resource acquisition, organizations frequently face several notable challenges. These include inaccurate demand forecasting and inflexible resource allocation mechanisms. Many organizations tend to enter into long-term contracts or acquire on-demand resources without a thorough understanding of their usage patterns, whichoften leads to cost overruns or resource under-utilization [2]. Addressing these challenges requires a more strategic approach to cloud resource management and financial planning. Cloud demand forecasting (CDF) uses historical data and usage patterns to enable enterprises to accurately predict future resource demands and forecast the time when resource pool utilization reaches procurement thresholds, thus optimizing procurement and allocation strategies and enabling rational procurement before resource depletion [3]. As shown in Figure 1, this process helps to ensure timely procurement before resource depletion. Furthermore, CDF provides data-driven decision-making support, empowering enterprises to make more informed decisions when navigating complex vendor pricing models and resource options. Studies [4] suggest that the implementation of precise cloud demand forecasting strategies can significantly improve resource utilization and simultaneously ensure business performance and reliability. However, accurately predicting long-term resource utilization remains challenging due to (1) highly dynamic demand trends influenced by business operations, (2) limited historical data in few-shot scenarios (e.g., 30-60 days of observations for newly deployed resource pools), and (3) the poor generalization in existing models, such as TimesFM, when confronted with distribution discrepancies across pools or extended prediction horizons. Traditional statistical models, such as ARIMA, rely on sufficient historical data for parameter estimation, and struggle when faced with short observation windows. Transformer-based deep learning models, while effective in large-scale data settings, often overfit in low-data regimes, limiting their generalization. TimesFM, a state-of-the-art time series foundation model, improves performance through pretraining and fine-tuning but still encounters significant failures when applied to cloud resource forecasting with highly heterogeneous and distributionally mismatched data across resource pools. To bridge these gaps, we propose DimAug-TimesFM, a dimension-augmented framework tailored to long-term cloud demand forecasting in few-shot settings. Our work is motivated by two critical observations. 1. Resource utilization patterns exhibit distinct phases (e.g., rapid growth during delivery vs. stabilized usage during maintenance), yet existing models lack mechanisms to prioritize phases reflecting actual demand;  
Figure 1: An example of CDF that enables users to predict the time when the resource pool utilization rate will reach the procurement threshold, so as to complete reasonable procurement before resource depletion. Cross-pool data augmentation, if improperly implemented, introduces artificial temporal features that degrade model performance. **Our core contributions are threefold:**  
1. **Delivery Period Extracting:** A novel method to identify resource delivery phases by analyzing smoothed utilization trends and differencing thresholds, enabling targeted modeling of periods critical for accurate forecasting;
2.