Results 241 to 250 of about 153,046 (296)
A comparison of change point detection methods for pest outbreak detection. [PDF]
Environ Syst DecisMa H, Leibowicz BD, Hasenbein JJ.
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Bayesian Exponential Smoothing [PDF]
, 2000 In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based on a state space model containing only a single source of error for each time interval. This model allows an improvement to current practices in exponential smoothing by providing both point predictions and measures of the uncertainty
Forbes, C.S., Snyder, R.D., Shami, R.S.
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Nodal Electricity Price Forecasting using Exponential Smoothing and Holt’s Exponential Smoothing
Distributed Generation & Alternative Energy Journal, 2023The prediction of nodal electricity price (NEP) is a primary step to be done before the bidding process starts in the actual market environment. NEP plays a significant role for the efficient working of the electrical system. NEP follows a common trend as during peak hours when the load is high the price will also be high similarly during off-peak-load
Md Irfan Ahmed, Ramesh Kumar
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Operations Research Letters, 1984
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Journal of Forecasting, 1992
AbstractThe paper is devoted to robust modifications of exponential smoothing for time series with outliers or long‐tailed distributions. Classical exponential smoothing applied to such time series is sensitive to the presence of outliers or long‐tailed distributions and may give inadequate smoothing and forecasting results.
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AbstractThe paper is devoted to robust modifications of exponential smoothing for time series with outliers or long‐tailed distributions. Classical exponential smoothing applied to such time series is sensitive to the presence of outliers or long‐tailed distributions and may give inadequate smoothing and forecasting results.
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Exponential smoothing: Estimation by maximum likelihood [PDF]
Journal of Forecasting, 1990 AbstractIn this paper several forecasting methods based on exponential smoothing with an underlying seasonal autoregressive‐moving average (SARIMA) model are considered. The relations between the smoothing constants and the coefficients of the autoregressive and moving average polynomials are used.
Broze, Laurence, Melard, Guy
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Proceedings of the workshop on Virtual environments 2003, 2003
We present novel algorithms for predictive tracking of user position and orientation based on double exponential smoothing. These algorithms, when compared against Kalman and extended Kalman filter-based predictors with derivative free measurement models, run approximately 135 times faster with equivalent prediction performance and simpler ...
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We present novel algorithms for predictive tracking of user position and orientation based on double exponential smoothing. These algorithms, when compared against Kalman and extended Kalman filter-based predictors with derivative free measurement models, run approximately 135 times faster with equivalent prediction performance and simpler ...
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