Workload Prediction on Google Cluster Trace

2. Related Works

A lot of researches have been conducted in forecasting time series. These studies have provided a better understanding of what might happen in future. Wang, Chau, Cheng and Qiu (2009) compared different artificial intelligence (AI) (such as SVM, ARIMA, ANFIS, ANN and GP) methods to build effective hydropower resource management and scheduling. They used four statistical performance measurement techniques to validate different AI models. The authors showed that AI methods are more powerful than traditional time series forecasting models. They authors reported that SVM had better result in both training and validation stage where ANFIS showed different performance in training and validation stage. Genetic Programming (GP) had better result in validation phase.

(Contreras) et. al. (2003) carefully studied the performance impact of the ARIMA model on next day electricity price forecasting. The authors proposed two different ARIMA models for predicting electricity price in Spain and California. In their research they showed that ARIMA model required only 5 hours to forecast future price for Spain, whereas it took 2 hours for prediction the price for California. Daz-Robles et al. (2008) designed a hybrid model by combining ARIMA and ANN model for forecasting air quality in urban area. They showed that traditional Box-Jenkins time series (ARIMA) model and multi-linear regression (MLR) models have limited accuracy. Khashei and Bijari (2010) built a novel predictive model by combing ARIMA and ANN model which gives better accuracy. They tested their model with three real-data set and argued that in case of higher performance this model can be used as appropriate alternative.