Autoregressive Time Series Forecasting of Computational Demand

We study the predictive power of autoregressive moving average models when forecasting demand in two shared computational networks, PlanetLab and Tycoon. Demand in these networks is very volatile, and predictive techniques to plan usage in advance can improve the performance obtained drastically. Our key finding is that a random walk predictor performs best for one-step-ahead forecasts, whereas ARIMA(1,1,0) and adaptive exponential smoothing models perform better for two and three-step-ahead forecasts. A Monte Carlo bootstrap test is proposed to evaluate the continuous prediction performance of different models with arbitrary confidence and statistical significance levels. Although the prediction results differ between the Tycoon and PlanetLab networks, we observe very similar overall statistical properties, such as volatility dynamics

Do We Need Experts for Time Series Forecasting?

This study examines a selection of off-the-shelf forecastingand forecast combination algorithms with a focus on assessing their practical relevance by drawing conclusions for non-expert users. Some of the methods have only recently been introduced and have not been part in comparative empirical evaluations before. Considering the advances of forecasting techniques, this analysis addresses the question whether we need human expertise for forecasting or whether the investigated methods provide comparable performance

Time series forecasting by principal covariate regression.

This paper is concerned with time series forecasting in the presence of a large numberof predictors. The results are of interest, for instance, in macroeconomic and financialforecasting where often many potential predictor variables are available. Most of thecurrent forecast methods with many predictors consist of two steps, where the largeset of predictors is first summarized by means of a limited number of factors -forinstance, principal components- and, in a second step, these factors and their lags areused for forecasting. A possible disadvantage of these methods is that the constructionof the components in the first step is not directly related to their use in forecasting inthe second step. This motivates an alternative method, principal covariate regression(PCovR), where the two steps are combined in a single criterion. This method hasbeen analyzed before within the framework of multivariate regression models. Moti-vated by the needs of macroeconomic time series forecasting, this paper discusses twoadjustments of standard PCovR that are necessary to allow for lagged factors and forpreferential predictors. The resulting nonlinear estimation problem is solved by meansof a method based on iterative majorization. The paper discusses some numericalaspects and analyzes the method by means of simulations. Further, the empirical per-formance of PCovR is compared with that of the two-step principal component methodby applying both methods to forecast four US macroeconomic time series from a set of132 predictors, using the data set of Stock and Watson (2005).distributed lags;dynamic factor models;economic forecasting;iterative majorization;principal components;principal covariate regression