The Experts below are selected from a list of 13479 Experts worldwide ranked by ideXlab platform
Ralph D. Snyder - One of the best experts on this subject based on the ideXlab platform.
-
forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal Exponential Smoothing
Omega-international Journal of Management Science, 2012Co-Authors: James W. Taylor, Ralph D. SnyderAbstract:This paper concerns the forecasting of seasonal intraday time series. An extension of Holt-Winters Exponential Smoothing has been proposed that smoothes an intraday cycle and an intraweek cycle. A recently proposed Exponential Smoothing method involves Smoothing a different intraday cycle for each distinct type of day of the week. Similar days are allocated identical intraday cycles. A limitation is that the method allows only whole days to be treated as identical. We introduce an Exponential Smoothing formulation that allows parts of different days of the week to be treated as identical. The result is a method that involves the Smoothing and initialisation of fewer terms than the other two Exponential Smoothing methods. We evaluate forecasting up to a day ahead using two empirical studies. For electricity load data, the new method compares well with a range of alternatives. The second study involves a series of arrivals at a call centre that is open for a shorter duration at the weekends than on weekdays. By contrast with the previously proposed Exponential Smoothing methods, our new method can model in a straightforward way this situation, where the number of periods on each day of the week is not the same.
-
Incorporating a Tracking Signal into State Space Models for Exponential Smoothing
2006Co-Authors: Ralph D. Snyder, Ann B KoehlerAbstract:It is a common practice to complement a forecasting method such as simple Exponential Smoothing with a monitoring scheme to detect those situations where forecasts have failed to adapt to structural change. It will be suggested in this paper that the equations for simple Exponential Smoothing can be augmented by a common monitoring statistic to provide a method that automatically adapts to structural change without human intervention. It is shown that the resulting equations conform to those of damped trend corrected Exponential Smoothing. In a similar manner, Exponential Smoothing with drift, when augmented by the same monitoring statistic, produces equations that split the trend into long term and short term components.
-
Exponential Smoothing model selection for forecasting
International Journal of Forecasting, 2006Co-Authors: Baki Billah, Ralph D. Snyder, Maxwell L King, Ann B KoehlerAbstract:Applications of Exponential Smoothing to forecast time series usually rely on three basic methods: simple Exponential Smoothing, trend corrected Exponential Smoothing and a seasonal variation thereof. A common approach to select the method appropriate to a particular time series is based on prediction validation on a withheld part of the sample using criteria such as the mean absolute percentage error. A second approach is to rely on the most appropriate general case of the three methods. For annual series this is trend corrected Exponential Smoothing: for sub-annual series it is the seasonal adaptation of trend corrected Exponential Smoothing. The rationale for this approach is that a general method automatically collapses to its nested counterparts when the pertinent conditions pertain in the data. A third approach may be based on an information criterion when maximum likelihood methods are used in conjunction with Exponential Smoothing to estimate the Smoothing parameters. In this paper, such approaches for selecting the appropriate forecasting method are compared in a simulation study. They are also compared on real time series from the M3 forecasting competition. The results indicate that the information criterion approach appears to provide the best basis for an automated approach to method selection, provided that it is based on Akaike's information criterion.
-
A Pedant's Approach to Exponential Smoothing
2005Co-Authors: Ralph D. SnyderAbstract:An approach to Exponential Smoothing that relies on a linear single source of error state space model is outlined. A maximum likelihood method for the estimation of associated Smoothing parameters is developed. Commonly used restrictions on the Smoothing parameters are rationalised. Issues surrounding model identification and selection are also considered. It is argued that the proposed revised version of Exponential Smoothing provides a better framework for forecasting than either the Box-Jenkins or the traditional multi-disturbance state space approaches.
-
Exponential Smoothing: A Prediction Error Decomposition Principle
2004Co-Authors: Ralph D. SnyderAbstract:In the Exponential Smoothing approach to forecasting, restrictions are often imposed on the Smoothing parameters which ensure that certain components are Exponentially weighted averages. In this paper, a new general restriction is derived on the basis that the one-step ahead prediction error can be decomposed into permanent and transient components. It is found that this general restriction reduces to the common restrictions used for simple, trend and seasonal Exponential Smoothing. As such, the prediction error argument provides the rationale for these restrictions.
James W. Taylor - One of the best experts on this subject based on the ideXlab platform.
-
forecasting intraday time series with multiple seasonal cycles using parsimonious seasonal Exponential Smoothing
Omega-international Journal of Management Science, 2012Co-Authors: James W. Taylor, Ralph D. SnyderAbstract:This paper concerns the forecasting of seasonal intraday time series. An extension of Holt-Winters Exponential Smoothing has been proposed that smoothes an intraday cycle and an intraweek cycle. A recently proposed Exponential Smoothing method involves Smoothing a different intraday cycle for each distinct type of day of the week. Similar days are allocated identical intraday cycles. A limitation is that the method allows only whole days to be treated as identical. We introduce an Exponential Smoothing formulation that allows parts of different days of the week to be treated as identical. The result is a method that involves the Smoothing and initialisation of fewer terms than the other two Exponential Smoothing methods. We evaluate forecasting up to a day ahead using two empirical studies. For electricity load data, the new method compares well with a range of alternatives. The second study involves a series of arrivals at a call centre that is open for a shorter duration at the weekends than on weekdays. By contrast with the previously proposed Exponential Smoothing methods, our new method can model in a straightforward way this situation, where the number of periods on each day of the week is not the same.
-
Multi-item sales forecasting with total and split Exponential Smoothing
Journal of the Operational Research Society, 2011Co-Authors: James W. TaylorAbstract:Efficient supply chain management relies on accurate demand forecasting. Typically,forecasts are required at frequent intervals for many items. Forecasting methods suitable for this application are those that can be relied upon to produce robust and accurate predictions when implemented within an automated procedure. Exponential Smoothing methods are a common choice. In this empirical case study paper, we evaluate a recently proposed seasonal Exponential Smoothing method that has previously been considered only for forecasting daily supermarket sales. We term this method ‘total and split’ Exponential Smoothing, and apply it to monthly sales data from a publishing company. The resulting forecasts are compared against a variety of methods, including several available in the software currently used by the company. Our results show total and split Exponential Smoothing outperforming the other methods considered. The results were also impressive for a method that trims outliers and then applies simple Exponential Smoothing.
Ann B Koehler - One of the best experts on this subject based on the ideXlab platform.
-
Incorporating a Tracking Signal into State Space Models for Exponential Smoothing
2006Co-Authors: Ralph D. Snyder, Ann B KoehlerAbstract:It is a common practice to complement a forecasting method such as simple Exponential Smoothing with a monitoring scheme to detect those situations where forecasts have failed to adapt to structural change. It will be suggested in this paper that the equations for simple Exponential Smoothing can be augmented by a common monitoring statistic to provide a method that automatically adapts to structural change without human intervention. It is shown that the resulting equations conform to those of damped trend corrected Exponential Smoothing. In a similar manner, Exponential Smoothing with drift, when augmented by the same monitoring statistic, produces equations that split the trend into long term and short term components.
-
Exponential Smoothing model selection for forecasting
International Journal of Forecasting, 2006Co-Authors: Baki Billah, Ralph D. Snyder, Maxwell L King, Ann B KoehlerAbstract:Applications of Exponential Smoothing to forecast time series usually rely on three basic methods: simple Exponential Smoothing, trend corrected Exponential Smoothing and a seasonal variation thereof. A common approach to select the method appropriate to a particular time series is based on prediction validation on a withheld part of the sample using criteria such as the mean absolute percentage error. A second approach is to rely on the most appropriate general case of the three methods. For annual series this is trend corrected Exponential Smoothing: for sub-annual series it is the seasonal adaptation of trend corrected Exponential Smoothing. The rationale for this approach is that a general method automatically collapses to its nested counterparts when the pertinent conditions pertain in the data. A third approach may be based on an information criterion when maximum likelihood methods are used in conjunction with Exponential Smoothing to estimate the Smoothing parameters. In this paper, such approaches for selecting the appropriate forecasting method are compared in a simulation study. They are also compared on real time series from the M3 forecasting competition. The results indicate that the information criterion approach appears to provide the best basis for an automated approach to method selection, provided that it is based on Akaike's information criterion.
-
Forecasting for Inventory Control with Exponential Smoothing
International Journal of Forecasting, 2002Co-Authors: Ralph D. Snyder, Ann B Koehler, J. Keith OrdAbstract:Exponential Smoothing, often used for sales forecasting in inventory control, has always been rationalized in terms of statistical models that possess errors with constant variances. It is shown in this paper that Exponential Smoothing remains the appropriate approach under more general conditions where the variances are allowed to grow and contract with corresponding movements in the underlying level. The implications for estimation and prediction are explored. In particular the problem of finding the prediction distribution of aggregate lead- time demand for use in inventory control calculations is considered. It is found that unless a drift term is added to simple Exponential Smoothing, the prediction distribution is largely unaffected by the variance assumption. A method for establishing order-up-to levels and reorder levels directly from the simulated prediction distributions is also proposed.
-
prediction intervals for Exponential Smoothing state space models
2001Co-Authors: Rob J Hyndman, Ann B Koehler, Ralph D. SnyderAbstract:The main objective of this paper is to provide analytical expression for forecast variances that can be used in prediction intervals for the Exponential Smoothing methods. These expressions are based on state space models with a single source of error that underlie the Exponential Smoothing methods. In cases where an ARIMA model also underlies an Exponential Smoothing method, there is an equivalent state space model with the same variance expression. We also discuss relationships between these new ideas and previous suggestions for finding forecast variances and prediction intervals for the Exponential Smoothing methods.
-
Lead Time demand for Simple Exponential Smoothing
1998Co-Authors: Ralph D. Snyder, Ann B Koehler, J. Keith OrdAbstract:A new simple formula is found to correct the underestimation of the standard deviation for total lead time demand when using simple Exponential Smoothing. This new formula allows one to see readily the significant size of the underestimation of the traditional formula and can easily be implemented in practice. The formula is derived by using a state space model for simple Exponential Smoothing.
Younghun Kim - One of the best experts on this subject based on the ideXlab platform.
-
Time Series Using Exponential Smoothing Cells
arXiv: Machine Learning, 2017Co-Authors: Avner Abrami, Aleksandr Y. Aravkin, Younghun KimAbstract:Time series analysis is used to understand and predict dynamic processes, including evolving demands in business, weather, markets, and biological rhythms. Exponential Smoothing is used in all these domains to obtain simple interpretable models of time series and to forecast future values. Despite its popularity, Exponential Smoothing fails dramatically in the presence of outliers, large amounts of noise, or when the underlying time series changes. We propose a flexible model for time series analysis, using Exponential Smoothing cells for overlapping time windows. The approach can detect and remove outliers, denoise data, fill in missing observations, and provide meaningful forecasts in challenging situations. In contrast to classic Exponential Smoothing, which solves a nonconvex optimization problem over the Smoothing parameters and initial state, the proposed approach requires solving a single structured convex optimization problem. Recent developments in efficient convex optimization of large-scale dynamic models make the approach tractable. We illustrate new capabilities using synthetic examples, and then use the approach to analyze and forecast noisy real-world time series. Code for the approach and experiments is publicly available.
Yingjie Yang - One of the best experts on this subject based on the ideXlab platform.
-
grey double Exponential Smoothing model and its application on pig price forecasting in china
Applied Soft Computing, 2016Co-Authors: Lifeng Wu, Yingjie YangAbstract:Graphical abstractDisplay Omitted HighlightsThe limit of traditional double Exponential Smoothing method is discussed.Grey accumulating generation operator is introduced into double Exponential Smoothing method.The examples demonstrated that GDES performs well in forecasting problems. To resolve the conflict between our desire for a good Smoothing effect and desire to give additional weight to the recent change, a grey accumulating generation operator that can smooth the random interference of data is introduced into the double Exponential Smoothing method. The results of practical numerical examples have demonstrated that the proposed grey double Exponential Smoothing method outperforms the traditional double Exponential Smoothing method in forecasting problems.
