Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests

The present study employs daily data made available by the STR SHARE Center covering the period from 1 January 2010 to 31 January 2020 for six Viennese hotel classes and their total. The forecast variable of interest is hotel room demand. As forecast models, (1) Seasonal Naïve, (2) Error Trend Seasonal (ETS), (3) Seasonal Autoregressive Integrated Moving Average (SARIMA), (4) Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), (5) Seasonal Neural Network Autoregression (Seasonal NNAR), and (6) Seasonal NNAR with an external regressor (seasonal naïve forecast of the inflation-adjusted ADR) are employed. Forecast evaluation is carried out for forecast horizons h = 1, 7, 30, and 90 days ahead based on rolling windows. After conducting forecast encompassing tests, (a) mean, (b) median, (c) regression-based weights, (d) Bates–Granger weights, and (e) Bates–Granger ranks are used as forecast combination techniques. In the relative majority of cases (i.e., in 13 of 28), combined forecasts based on Bates–Granger weights and on Bates–Granger ranks provide the highest level of forecast accuracy in terms of typical measures. Finally, the employed methodology represents a fully replicable toolkit for practitioners in terms of both forecast models and forecast combination techniques.

Single Versus Combined Forecasting: The Case Of Wind Run Data

This article compared single to combined forecasts of wind run using artificial neural networks, decomposition, Holt-Winters’ and SARIMA models. The artificial neural networks utilized the feedback framework while decomposition and Holt-Winters’ approaches utilized their multiplicative versions. Holt-Winters’ performed best of single models but ranked fourth, of all fifteen models (single and combined). The combination of decomposition and Holt-Winters’ models ranked best of all two-model combinations and second of all models. Combination of decomposition, Holt-Winters’ and SARIMA performed best of three-model combinations and ranked first, of all models. The only combination of four models ranked third of all models. The accuracy of single forecast should not be underestimated as a single model (Holt-Winters’) outperformed eleven combined models. It is therefore, evident that inclusion of additional model forecast does not necessarily improve combined forecast accuracy. In any modeling situation, single and combined forecasts should be allowed to compete.

Predictive Models of Accidents at Work in the Steel Sector as a Framework for Sustainable Safety

Work safety can be a component of the broadly understood sustainable enterprise approach that goes beyond the idea of sustainable development. Sustainability in an unpredictable and turbulent environment has many constellations, many aspects and many fields of the enterprise’s activity and it complements the rationality of the business. The aim is to understand the sustainability of safety, because this is the term we have adopted for rationality in occupational safety management, in the context of the analysis of work accidents in the Polish steel industry, with particular emphasis on the methodology of forecast assessment in the studied area, proposed by us. The realized forecasts were used for the creation of a combined model which formed the basis for formulating conclusions from the analysis. The publication presents the modeling of the victims of work accidents in the steel sector in Poland. Based on the research of the forecasts obtained, a downward trend is recorded in the number of persons injured in accidents at work in the steel sector. In order to select the optimal model, it was proposed to set combined forecasts. In order to select the optimal model, it was proposed to set combined forecasts. The obtained values of ex-ante forecasts in the combined model also confirmed the forecasted trends determined within the adaptation models. The study is a proposal to extend the combined forecasting methods used to assess occupational safety. We consciously chose to include the methodology of combined forecasting of the number of people injured in accidents in the interpretation of sustainability, because we see the possibility of interpreting accident rates in sustainable business in the future. In the publication, we propose the framework of the sustainable safety model as an element of work safety management in an enterprise. We are trying to answer the question about the place of accident prediction in sustainable safety.

SIMPLE BAYESIAN FORECAST COMBINATION

In this paper, it is proposed to combine the forecasts using a simple Bayesian forecast combination algorithm. The algorithm is applied to forecasts from three non-nested diffusion models for S shaped processes like virus diffusion. An illustration to daily data on first-wave cumulative Covid-19 cases in the Netherlands shows the ease of use of the algorithm and the accuracy of the newly combined forecasts.

Wisdom of the expert crowd prediction of response for 3 neurology randomized trials

ObjectiveTo explore the accuracy of combined neurology expert forecasts in predicting primary endpoints for trials.MethodsWe identified one major randomized trial each in stroke, multiple sclerosis (MS), and amyotrophic lateral sclerosis (ALS) that was closing within 6 months. After recruiting a sample of neurology experts for each disease, we elicited forecasts for the primary endpoint outcomes in the trial placebo and treatment arms. Our main outcome was the accuracy of averaged predictions, measured using ordered Brier scores. Scores were compared against an algorithm that offered noncommittal predictions.ResultsSeventy-one neurology experts participated. Combined forecasts of experts were less accurate than a noncommittal prediction algorithm for the stroke trial (pooled Brier score = 0.340, 95% subjective probability interval [sPI] 0.340 to 0.340 vs 0.185 for the uninformed prediction), and approximately as accurate for the MS study (pooled Brier score = 0.107, 95% confidence interval [CI] 0.081 to 0.133 vs 0.098 for the noncommittal prediction) and the ALS study (pooled Brier score = 0.090, 95% CI 0.081 to 0.185 vs 0.090). The 95% sPIs of individual predictions contained actual trial outcomes among 44% of experts. Only 18% showed prediction skill exceeding the noncommittal prediction. Independent experts and coinvestigators achieved similar levels of accuracy.ConclusionIn this first-of-kind exploratory study, averaged expert judgments rarely outperformed noncommittal forecasts. However, experts at least anticipated the possibility of effects observed in trials. Our findings, if replicated in different trial samples, caution against the reliance on simple approaches for combining expert opinion in making research and policy decisions.

Forecasting virus diffusion with simple Bayesian forecast combination

There are various diffusion models for S shaped processes like virus diffusion and these models are typically not nested. In this note it is proposed to combine the forecasts using a simple Bayesian forecast combination algorithm. An illustration to daily data on cumulative Covid-19 cases in the Netherlands shows the ease of use of the algorithm and the accuracy of the thus combined forecasts.

A comparative study on combinations of forecasts and their individual forecasts by means of simulated series

Over the years, several studies that compare individual forecasts with the combination of forecasts were published. There is, however, no unanimity in the conclusions. Furthermore, methods of combination by regression are poorly explored. This paper presents a comparative study of three methods of combination and their individual forecasts. Based on simulated data, it is evaluated the accuracy of Artificial Neural Networks, ARIMA and exponential smoothing models; calculating the combined forecasts through simple average, minimum variance and regression methods. Four accuracy measurements, MAE, MAPE, RMSE and Theil’s U, were used for choosing the most accurate method. The main contribution is the accuracy of the combination by regression methods.

THE APPLICATION OF RIDGE REGRESSION METHODS WHEN COMBINING FORECASTS

Forecasting of economic indicators with time series using one or another method or another but the only method leads to the situation that all the information contained in other forecasting methods is usually discarded. The information that is ignored may contain information that allows other features of the economic process to be assessed. Combining forecasts makes possible to take into account almost all the information contained in particular forecasts. In the article, we present the analysis of the application of the method of regression analysis, in particular, ridge regression for finding the weighting coefficients of the particular forecasts in the combined forecast. We compared the accuracy of prediction based on the ridge regression with other methods of combining predictions. The purpose of our research work was an analysis of the most common methods of combining forecasts — various modifications of Granger-Ramanathan methods and comparison with a new approach of combining forecasts based on the ridge regression for its use in practice. We used statistical methods of time series forecasting (the method of harmonic weights, adaptive exponential smoothing using a tracking signal, the method of simple exponential smoothing and the Box-Jenkins model), the method of constructing combined forecasts, as well as methods of regression analysis. As a result, we built the combined forecasts based on annual data for the period from 1950 to 2015 on the production in Russia of some products: steel, metallurgical coke, pulp, plywood, cement. We used the methods of Granger-Ramanathan (without restrictions and with restrictions on the sum of coefficients in partial predictions) and also the ∆-coefficients obtained by the ridge regression method. The forecasts constructed using the Granger-Ramanathan methods give the highest accuracy of the combined forecast. The method based on the ridge regression is less accurate, but better than the separate predictions. At the same time, the proposed method of calculating the weight coefficients on the basis of the ridge regression has a well- developed scheme of calculation and eliminates the negative weight coefficients in the combined forecast.