A b s t r a c t O n e w a y t o c o n t r a s t t h e b

Pattern recognition techniques for time-series forecasting are beginning to be realised as an important tool for predicting chaotic behaviour of dynamic systems. In this paper we develop the concept of a Pattern Modelling and Recognition System which is used for predicting future behaviour of time-series using local approximation. In this paper we compare this forecasting tool with neural netwo...

A new short-term time series forecasting method based on the identification of skeleton algebraic sequences is proposed in this paper. The concept of the rank of the Hankel matrix is exploited to detect a base fragment of the time series and to extrapolate the model of the process into future. Evolutionary algorithms are used to remove the noise, to identify the skeleton algebraic sequence and ...

In the last decade, bio-inspired methods have gained an increasing acceptation as alternative approaches for Time Series Forecasting. Indeed, the use of tools such as Artificial Neural Networks (ANNs) and Genetic and Evolutionary Algorithms (GEAs), introduced important features to forecasting models, taking advantage of nonlinear learning and adaptive search. In the present approach, a combinat...

The forecasting problem for a stationary and ergodic binary time series {Xn}n=0 is to estimate the probability that Xn+1 = 1 based on the observations Xi, 0 ≤ i ≤ n without prior knowledge of the distribution of the process {Xn}. It is known that this is not possible if one estimates at all values of n. We present a simple procedure which will attempt to make such a prediction infinitely often ...

Copula-based models provide a great deal of ‡exibility in modelling multivariate distributions, allowing the researcher to specify the models for the marginal distributions separately from the dependence structure (copula) that links them to form a joint distribution. In addition to ‡exibility, this often also facilitates estimation of the model in stages, reducing the computational burden. Thi...

Automatic forecasts of univariate time series are largely demanded in business and science. In this paper, we investigate the forecasting task for geo-referenced time series. We take into account the temporal and spatial dimension of time series to get accurate forecasting of future data. We describe two algorithms for forecasting which ARIMA models. The first is designed for seasonal data and ...

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...

In this paper, we propose a generic non-linear approach for time series forecasting. The main feature of this approach is the use of a simple statistical forecasting in small regions of an input space adequately chosen and quantized. The partition of the space is achieved by the Kohonen algorithm. The method is then applied to a widely known time-series from the SantaFe competition, and the res...

We review the past 25 years of time series research that has been published in journals managed by the International Institute of Forecasters (Journal of Forecasting 1982–1985; International Journal of Forecasting 1985–2005). During this period, over one third of all papers published in these journals concerned time series forecasting. We also review highly influential works on time series fore...