# DimAug-TimesFM: Dimension Augmentation for Long-Term Cloud Demand Forecasting in Few-Shot Scenarios
## 6 Conclusions and Future Work  
This study addresses the critical challenge of long-term cloud demand forecasting in few-shot scenarios through the proposed DimAug-TimesFM framework. By integrating Delivery Period Extracting and Dimension-Augmented TimesFM, the framework effectively mitigates dynamic demand trends, limited historical data, and distribution discrepancies inherent in cloud resource utilization. The dimension augmentation strategy enriches training data by aligning cross-pool time series via DTW-based similarity matching, thereby enhancing the generalization of the pretrained TimesFM model. Extensive experiments on real-world cloud resource data from a Chinese telecom operator demonstrate the framework's superiority over state-of-the-art baselines (e.g., TimesFM, DLinear, PatchTST), achieving average reductions of 73.7-82.1% in MAE, 73.8-79.1% in MAPE, and 72.9-81.7% in RMSE across short-term (16-day) and long-term (64- to 128-day) forecasting tasks. Furthermore, the framework exhibits exceptional robustness in scenarios where traditional models fail, underscoring the synergy between temporal feature enhancement and cross-pool data augmentation.  
Although DimAug-TimesFM demonstrates significant improvements, several research directions are worth further exploration.  
1. **Integration of External Factors:** Future efforts will incorporate quantifiable external drivers, such as business growth metrics (e.g., quarterly revenue, user acquisition rates), and seasonal indices (e.g., holiday-induced workload fluctuations) into the framework. This will extend DimAug-TimesFM to multivariate forecasting, enhancing its ability to model demand dynamics under real-world constraints.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**TimesFM**} & \multicolumn{4}{c}{**Ours**} \\ \cline{3-8}  & & **MAE** & **MAPE** & **RMSE** & **MAE** & **MAPE** & **RMSE** \\ \hline \multirow{2}{*}{Resource\_Pool} & 190\_64 & 69.2020 & 20,030.6820 & 83.1930 & 0.1347 & 39.0636 & 0.1514 \\  & 229\_128 & 121.0135 & 24,054.3601 & 149.4365 & 0.6453 & 128.2051 & 0.7477 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Robustness evaluation of DimAug-TimesFM on typical resource pools with TimesFM performance anomalies.  
2. **Multi-Resource Forecasting**: Although the framework is theoretically extensible to the concurrent prediction of heterogeneous resources (e.g., CPU, memory, storage), empirical validation is required to assess its capability in capturing cross-resource interdependencies. Planned work includes constructing multi-resource time series datasets and introducing composite metrics like Joint RMSE (J-RMSE) to evaluate prediction coherence.
3. **Generalizability Enhancement**: Applying dimension augmentation to state-of-the-art large time series models (e.g., Chronos, Timer) and validation on diverse few-shot datasets will further validate the robustness of the framework. These extensions aim to strengthen the framework's adaptability to more real-world application scenarios and advance scalable and sustainable FinOps practices.  
This research received no external funding.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflicts of interest.