In order to prevent violation of service-level objectives and to guarantee good user experience, detection of symptoms such as slow application response, degraded transaction throughput, and service outages, is crucial. We propose a black-box approach for detecting such symptoms in service performance behaviour without intrusive application instrumentation. In case a known baseline behaviour ex...

Time series prediction techniques have been shown to significantly reduce the radio use and energy consumption of wireless sensor nodes performing periodic data collection tasks. In this paper, we propose an implementation of exponential smoothing, a standard time series prediction technique, for wireless sensors. We rely on a framework called Adaptive Model Selection (AMS), specifically design...

Time series and their methods of analysis are important subjects in statistics. Most of time series have a linear behavior and can be modelled by linear ARIMA models. However, some of realized time series have a nonlinear behavior and for modelling them one needs nonlinear models. For this, many good parametric nonlinear models such as bilinear model, exponential autoregressive model, threshold...

This paper introduces a new smoothing algorithm for time series processing to be implemented in various applications, including, among others, array processing and wireless communication. This new approach has also been tested for noise filtering and edge preserving in images. The algorithm is based on a nonlinear exponential smoothing (NLES) method that shows a significant improvement in the m...

— During the past decade Régression Analysis has gained wide acceptance as a method for preparing medium and long range forecasts for time series. However/for a shortterm forecasting situation or when the number of observations is small, régression analysis is costly and of ten impractical. Exponential smoothing is the forecasting method most of ten used in these latter situations, but it has s...

The purpose of this study is to compare the 3 forecasting methods Linear Regression, Exponential Smoothing and Weighted Moving Average based on smallest error value or close zero. From results study, Regression method was obtained as correct with a predicted 502 students, MAD 27.83, MSE 1152.1 MAPE 8.1%. Tracking Signal moves between 1 -1, movement within control limits tracking signal standard...

Robust versions of the exponential and Holt–Winters smoothing method for forecasting are presented. They are suitable for forecasting univariate time series in the presence of outliers. The robust exponential and Holt–Winters smoothing methods are presented as recursive updating schemes that apply the standard technique to pre-cleaned data. Both the update equation and the selection of the smoo...

Exponential smoothing methods gave poor forecast accuracy in Fildes et al.’s study of telecommunications time series. We reexamine this study and show that parameter optimization improves the accuracy of the Holt and damped trend methods. Further improvement occurs when the time series are trimmed to eliminate irrelevant early data, and when the methods are fitted to minimize the MAD rather tha...