Unmasking the Theta Method.

The Theta method of forecasting performed particularly well in the M3-competition and is therefore of interest to forecast practitioners. The description of the method given by Assimakopoulos and Nikolopoulos (2000) involves several pages of algebraic manipulation and is difficult to comprehend. We show that the method can be expressed much more simply; furthermore we show that the forecasts obtained are equivalent to simple exponential smoothing with drift.exponential smoothing; forecasting competitions; state space models

Empirical Information Criteria for Time Series Forecasting Model Selection

In this paper, we propose a new Empirical Information Criterion (EIC) for model selection which penalizes the likelihood of the data by a function of the number of parameters in the model. It is designed to be used where there are a large number of time series to be forecast. However, a bootstrap version of the EIC can be used where there is a single time series to be forecast. The EIC provides a data-driven model selection tool that can be tuned to the particular forecasting task. We compare the EIC with other model selection criteria including Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The comparisons show that for the M3 forecasting competition data, the EIC outperforms both the AIC and BIC, particularly for longer forecast horizons. We also compare the criteria on simulated data and find that the EIC does better than existing criteria in that case also.Exponential smoothing; forecasting; information criteria; M3 competition; model selection.

Exponential Smoothing Model Selection for Forecasting

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.Model Selection; Exponential Smoothing; Information Criteria; Prediction; Forecast Validation

Computation of electron-impact K-shell ionization cross sections of atoms

The total cross sections of electron impact single K-shell ionization of atomic targets, with a wide range of atomic numbers from Z=6-50, are evaluated in the energy range up to about 10 MeV employing the recently proposed modified version of the improved binary-encounter dipole (RQIBED) model [Uddin , Phys. Rev. A 70, 032706 (2004)], which incorporates the ionic and relativistic effects. The experimental cross sections for all targets are reproduced satisfactorily even in the relativistic energies using fixed generic values of the two parameters in the RQIBED model. The relativistic effect is found to be significant in all targets except for C, being profound in Ag and Sn

Avoidance of multicast incapable branching nodes for multicast routing in WDM networks

In this articlewestudy themulticast routing problem in all-opticalWDMnetworks under the spare light splitting constraint. To implement a multicast session, several light-trees may have to be used due to the limited fanouts of network nodes. Although many multicast routing algorithms have been proposed in order to reduce the total number of wavelength channels used (total cost) for a multicast session, the maximum number of wavelengths required in one fiber link (link stress) and the end-to-end delay are two parameters which are not always taken into consideration. It is known that the shortest path tree (SPT) results in the optimal end-to-end delay, but it can not be employed directly for multicast routing in sparse light splitting WDM networks. Hence, we propose a novel wavelength routing algorithm which tries to avoid the multicast incapable branching nodes (MIBs, branching nodes without splitting capability) in the shortest-path-based multicast tree to diminish the link stress. Good parts of the shortest-path-tree are retained by the algorithm to reduce the end-to-end delay. The algorithm consists of tree steps: (1) aDijkstraPro algorithmwith priority assignment and node adoption is introduced to produce a SPT with up to 38% fewer MIB nodes in the NSF topology and 46% fewerMIB nodes in the USA Longhaul topology, (2) critical articulation and deepest branch heuristics are used to process the MIB nodes, (3) a distance-based light-tree reconnection algorithm is proposed to create the multicast light-trees. Extensive simulations demonstrate the algorithm's efficiency in terms of link stress and end-to-end delay

Protocol for a randomised controlled trial of a family strengthening program to prevent unhealthy weight gain among 5 to 11-year-old children from at-risk families : the Strong Families Trial

Background: Obesity is an increasing health concern in Australia among adult and child populations alike and is often associated with other serious comorbidities. While the rise in the prevalence of childhood obesity has plateaued in high-income countries, it continues to increase among children from disadvantaged and culturally diverse backgrounds. The family environment of disadvantaged populations may increase the risk of childhood obesity through unhealthy eating and lifestyle practices. The Strong Families Trial aims to assess the effectiveness of a mixed behavioural and lifestyle intervention for parents and carers of at-risk populations, i.e. families from culturally diverse and disadvantaged backgrounds, in preventing unhealthy weight gain among children aged 5 to 11 years. Methods: Eight hundred families from low socio-economic areas in Greater Western Sydney, NSW, and Melbourne, VIC, will be recruited and randomised into a lifestyle intervention or control group. The intervention comprises 90-minute weekly sessions for 6 weeks (plus two-booster sessions) of an integrated, evidence-based, parenting and lifestyle program that accounts for the influences of family functioning. Primary (anthropometric data) and secondary (family functioning, feeding related parenting, physical activity, consumption of healthy foods, health literacy, family and household costs) outcome measures will be assessed at baseline, immediately following the intervention, and 12 months post-intervention. Discussion: This study will elucidate methods for engaging socially disadvantaged and culturally diverse groups in parenting programs concerned with child weight status. Trial Registration: This study is registered with the Australian New Zealand Clinical Trials Registry (ACTRN12619001019190). Registered 16 July 2019

Evaluation of bottom-up and top-down strategies for aggregated forecasts: state space models and arima applications

Abstract. In this research, we consider monthly series from the M4 competition to study the relative performance of top-down and bottom-up strategies by means of implementing forecast automation of state space and ARIMA models. For the bottomup strategy, the forecast for each series is developed individually and then these are combined to produce a cumulative forecast of the aggregated series. For the top-down strategy, the series or components values are first combined and then a single forecast is determined for the aggregated series. Based on our implementation, state space models showed a higher forecast performance when a top-down strategy is applied. ARIMA models had a higher forecast performance for the bottom-up strategy. For state space models the top-down strategy reduced the overall error significantly. ARIMA models showed to be more accurate when forecasts are first determined individually. As part of the development we also proposed an approach to improve the forecasting procedure of aggregation strategies