Evolving time series forecasting ARMA models

Nowadays, the ability to forecast the future, based only on past data, leads to strategic advantages, which may be the key to success in organizations. Time Series Forecasting (TSF) allows the modeling of complex systems as ``black-boxes'', being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popular. The present work reports on a two-level architecture, where a (meta-level) binary EA will search for the best AutoRegressive Moving-Average (ARMA) model, being the parameters optimized by a (low-level) EA, which encodes real values. The handicap of this approach is compared with conventional forecasting methods, being competitive

Forecasting Time Series with VARMA Recursions on Graphs

Graph-based techniques emerged as a choice to deal with the dimensionality issues in modeling multivariate time series. However, there is yet no complete understanding of how the underlying structure could be exploited to ease this task. This work provides contributions in this direction by considering the forecasting of a process evolving over a graph. We make use of the (approximate) time-vertex stationarity assumption, i.e., timevarying graph signals whose first and second order statistical moments are invariant over time and correlated to a known graph topology. The latter is combined with VAR and VARMA models to tackle the dimensionality issues present in predicting the temporal evolution of multivariate time series. We find out that by projecting the data to the graph spectral domain: (i) the multivariate model estimation reduces to that of fitting a number of uncorrelated univariate ARMA models and (ii) an optimal low-rank data representation can be exploited so as to further reduce the estimation costs. In the case that the multivariate process can be observed at a subset of nodes, the proposed models extend naturally to Kalman filtering on graphs allowing for optimal tracking. Numerical experiments with both synthetic and real data validate the proposed approach and highlight its benefits over state-of-the-art alternatives.Comment: submitted to the IEEE Transactions on Signal Processin

Managing Uncertainty: A Case for Probabilistic Grid Scheduling

The Grid technology is evolving into a global, service-orientated architecture, a universal platform for delivering future high demand computational services. Strong adoption of the Grid and the utility computing concept is leading to an increasing number of Grid installations running a wide range of applications of different size and complexity. In this paper we address the problem of elivering deadline/economy based scheduling in a heterogeneous application environment using statistical properties of job historical executions and its associated meta-data. This approach is motivated by a study of six-month computational load generated by Grid applications in a multi-purpose Grid cluster serving a community of twenty e-Science projects. The observed job statistics, resource utilisation and user behaviour is discussed in the context of management approaches and models most suitable for supporting a probabilistic and autonomous scheduling architecture

Orion Routing Protocol for Delay-Tolerant Networks

In this paper, we address the problem of efficient routing in delay tolerant network. We propose a new routing protocol dubbed as ORION. In ORION, only a single copy of a data packet is kept in the network and transmitted, contact by contact, towards the destination. The aim of the ORION routing protocol is twofold: on one hand, it enhances the delivery ratio in networks where an end-to-end path does not necessarily exist, and on the other hand, it minimizes the routing delay and the network overhead to achieve better performance. In ORION, nodes are aware of their neighborhood by the mean of actual and statistical estimation of new contacts. ORION makes use of autoregressive moving average (ARMA) stochastic processes for best contact prediction and geographical coordinates for optimal greedy data packet forwarding. Simulation results have demonstrated that ORION outperforms other existing DTN routing protocols such as PRoPHET in terms of end-to-end delay, packet delivery ratio, hop count and first packet arrival

Long-run marketing inferences from scanner data.

Good marketing decisions require managers' understanding of the nature of the market-response function relating performance measures such as sales and market share to variations in the marketing mix (product, price, distribution and communications efforts). Our paper focuses on the dynamic aspect of market-response functions, i.e. how current marketing actions affect current and future market response. While conventional econometrics has been the dominant methodology in empirical market-response analyses, time-series analysis offers unique opportunities for pushing the frontier in dynamic research. This paper examines the contributions an d the future outlook of time-series analysis in market-response modeling. We conclude first, that time series analysis has made a relatively limited overall contribution to the discipline, and investigate reasons why that has been the case. However, major advances in data (transactions-based databases and in modeling technology (long-term time-series modeling) create new opportunities for time-series techniques in marketing, in particular for the study of long-run marketing effectiveness. We discuss four major aspects of long -term time-series modeling, relate them to substantive marketing problems, and describe some early applications. Combining the new data with the new methods, we then present original empirical results on the long-term behavior of brand sales and category sales for four consumer products. We discuss the implications of our findings for future research in market response. Our observations lead us to identify three areas where additional research could enhance the diffusion of the identified time-series concepts in marketing.Data; Marketing;

Forecasting international bandwidth capability

M-competition studies provide a set of stylized recommendations to enhance forecast reliability. However, no single method dominates across series, leading to consideration of the relationship between selected data characteristics and the reliability of alternative forecast methods. This study conducts an analysis of predictive accuracy in relation to Internet bandwidth loads. Extrapolation techniques that perform best in M-competitions perform relatively poorly in predicting Internet bandwidth loads. Such performance is attributed to Internet bandwidth data exhibiting considerably less structure than M-competition data.Bandwidth; forecast comparisons

Using wavelets for time series forecasting: Does it pay off?

By means of wavelet transform a time series can be decomposed into a time dependent sum of frequency components. As a result we are able to capture seasonalities with time-varying period and intensity, which nourishes the belief that incorporating the wavelet transform in existing forecasting methods can improve their quality. The article aims to verify this by comparing the power of classical and wavelet based techniques on the basis of four time series, each of them having individual characteristics. We find that wavelets do improve the forecasting quality. Depending on the data's characteristics and on the forecasting horizon we either favour a denoising step plus an ARIMA forecast or an multiscale wavelet decomposition plus an ARIMA forecast for each of the frequency components. --Forecasting,Wavelets,ARIMA,Denoising,Multiscale Analysis