Prediction with univariate time series models: The Iberia case

In this paper we model the monthly number of passengers flying with the Spanish airline IBERIA from January 1985 to December 1992 and predict future values of the series up to October 1994. This series is characterized by strong seasonal variations and by having an upward trend which has a rupture during 1990 with the slope changing to be negative. We compare observed values with predictions made by a deterministic components model, the Holt-Winters exponential smoothing filter, an ARIMA model and a structural time series model. As expected, we show that the deterministic components model is too rigid in the presence fo breaks in trends although surprisingly the within-sample fit is better than for any of the other models considered. With respect to Holt-Winters predictions, they fail because they are not able to acommodate outliers. Finally, ARIMA and structural models are shown to have very similar prediction performance, being very flexible to predict reasonably well when there are changes in trend and outliers.ARIMA models, Breaks in trends, Deterministic components, Holt-Winters algorithm, Outliers, Intervention analysis, Structural time series models, Unobserved components models.

Time Series Analysis of Non-Gaussian Observations Based on State Space Models from Both Classical and Bayesian Perspectives

The analysis of non-Gaussian time series using state space models is considered from both classical and Bayesian perspectives. The treatment in both cases is based on simulation using importance sampling and antithetic variables; Monte Carlo Markov chain methods are not employed. Non-Gaussian disturbances for the state equation as well as for the observation equation are considered. Methods for estimating conditional and posterior means of functions of the state vector given the observations, and the mean square errors of their estimates, are developed. These methods are extended to cover the estimation of conditional and posterior densities and distribution functions. Choice of importance sampling densities and antithetic variables is discussed. The techniques work well in practice and are computationally effcient. Their use is illustrated by applying to a univariate discrete time series, a series with outliers and a volatility series.Antithetic variables;Conditional and posterior statistics;Exponential family distributions;Heavy-tailed distributions;Importance sampling;Kalman filtering and smoothing;Monte Carlo simulation;Non-Gaussian time series models;Posterior distributions

Robust exponential smoothing of multivariate time series.

Multivariate time series may contain outliers of different types. In presence of such outliers, applying standard multivariate time series techniques becomes unreliable. A robust version of multivariate exponential smoothing is proposed. The method is affine equivariant, and involves the selection of a smoothing parameter matrix by minimizing a robust loss function. It is shown that the robust method results in much better forecasts than the classic approach in presence of outliers, and performs similar when the data contain no outliers. Moreover, the robust procedure yields an estimator of the smoothing parameter less subject to downward bias. As a byproduct, a cleaned version of the time series is obtained, as is illustrated by means of a real data example.Data cleaning; Exponential smoothing; Forecasting; Multivariate time series; Robustness;

Forecasting telecommunications data with linear models

For telecommunication companies to successfully manage their business, companies rely on mapping future trends and usage patterns. However, the evolution of telecommunications technology and systems in the provision of services renders imperfections in telecommunications data and impinges on a company’s’ ability to properly evaluate and plan their business. ITU Recommendation E.507 provides a selection of econometric models for forecasting these trends. However, no specific guidance is given. This paper evaluates whether simple extrapolation techniques in Recommendation E.507 can generate accurate forecasts. Standard forecast error statistics—mean absolute percentage error, median absolute percentage error and percentage better—show the ARIMA, Holt and Holt-D models provide better forecasts than a random walk and other linear extrapolation methods.linear models; ITU Recommendations; telecommunications forecasting

Robust Forecasting of Non-Stationary Time Series

This paper proposes a robust forecasting method for non-stationary time series. The time series is modelled using non-parametric heteroscedastic regression, and fitted by a localized MM-estimator, combining high robustness and large efficiency. The proposed method is shown to produce reliable forecasts in the presence of outliers, non-linearity, and heteroscedasticity. In the absence of outliers, the forecasts are only slightly less precise than those based on a localized Least Squares estimator. An additional advantage of the MM-estimator is that it provides a robust estimate of the local variability of the time series.Heteroscedasticity;Non-parametric regression;Prediction;Outliers;Robustness

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