In high-performance computing (HPC) platform, resource usage pattern changes over time which makes the resource monitoring a challenge. Maintaining the performance goals within a good power range is very critical where servers suffer from under utilisation, failure or degraded hardware support. Better forecast of the workload can reduce the energy cost by predicting the future workload more accurately. Identifying the usage pattern is also vital for efficient capacity planning because prediction can also be augmented with an effective resource allocation strategy to manage resource distribution goals. However, a single prediction model does not fit for all. In this paper, we compare forecast performance of the state-of-the-art ARMA class (integrated ARMA (ARIMA), seasonal integrated ARMA (SARIMA) and fractionally integrated ARMA (ARFIMA)) with the singular spectrum analysis (SSA) method using CPU, RAM and Network traces collected from Wikimedia grid. We found that the most simple model of ARMA class (ARIMA) had outperformed other complex ARMA class models while forecasting the bursty pattern of Networks. ARIMA model provides the best forecast for the Network data while SSA is found to be the best method for CPU and RAM. We also show that with proper model fitting, we can achieve high forecasting precision as low as 0.00586% for RAM and maximum error around 5% for Network without having complete information about the underlying system hardware and the running applications type.