Tourism Demand Forecasting Using Nonadditive Forecast Combinations

Accurately forecasting the demand for tourism can help governments formulate industrial policies and guide the business sector in investment planning. Combining forecasts can improve the accuracy of forecasting the demand for tourism, but limited work has been devoted to developing such combinations. This article addresses two significant issues in this context. First, the linear combination is the commonly used method of combining tourism forecasts. However, additive techniques unreasonably ignore interactions among the inputs. Second, the available data often do not adhere to specific statistical assumptions. Grey prediction has thus drawn attention because it does not require that the data follow any statistical distribution. This study proposes a nonadditive combination method by using the fuzzy integral to integrate single-model forecasts obtained from individual grey prediction models. Using China and Taiwan tourism demand as empirical cases, the results show that the proposed method outperforms the other combined methods considered here.

A hybrid MIDAS approach for forecasting hotel demand using large panels of search data

The large amounts of hospitality and tourism-related search data sampled at different frequencies have long presented a challenge for hospitality and tourism demand forecasting. This study aims to evaluate the applicability of large panels of search series sampled at daily frequencies to improve the forecast precision of monthly hotel demand. In particular, a hybrid mixed-data sampling regression approach integrating a dynamic factor model and forecast combinations is the first reported method to incorporate mixed-frequency data while remaining parsimonious and flexible. A case study is undertaken by investigating Sanya, the southernmost city in Hainan province, as a tourist destination using 9 years of the experimental data set. Dynamic factor analysis is used to extract the information from large panels of web search series, and forecast combinations are attempted to obtain the final prediction results of the individual forecasts to enhance the prediction accuracy further. The empirical analysis results suggest that the developed hybrid forecast approach leads to improvements in monthly forecasts of hotel occupancy over its competitors.

Towards benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables in predictive modelling contexts

<p>We discuss possible pathways towards reducing uncertainty in predictive modelling contexts in hydrology. Such pathways may require big datasets and multiple models, and may include (but are not limited to) large-scale benchmark experiments, forecast combinations, and predictive modelling frameworks with hydroclimatic time series analysis and clustering inputs. Emphasis is placed on the newest concepts and the most recent methodological advancements for benefitting from diverse inferred features and foreseen behaviours of hydroclimatic variables, derived by collectively exploiting diverse essentials of studying and modelling hydroclimatic variability and change (from both the descriptive and predictive perspectives). Our discussions are supported by big data (including global-scale) investigations, which are conducted for several hydroclimatic variables at several temporal scales.</p>

Previsão da taxa de incidência dos casos de AIDS no município de Santa Maria – RS

The spread of AIDS was a striking social fact in the late twentieth century, mainly due to the lack of knowledge of sexually active people and drug users, spreading rapidly across five continents. Initially, it was associated with the group of male homosexuals. Over the years, other population segments became infected with the Human Immunodeficiency Virus (HIV). Thus, this article aims to compare forecasting methodologies to predict the incidence rate of AIDS per 100,000 inhabitants in Santa Maria between 2017 and 2022. For this purpose, two forecasting models were adjusted for each series (polynomial trend model plus an ARIMA model (p, d, q), and an exponential smoothing model). As the series show structural breakdowns due to various historical events in Brazil and around the world, prediction combinations methodologies were used through robust regressions, using the Weighted Least Squares, MM and Quantile Regression methods. We verified through the accuracy measures that, for men, the best forecasting methodologies were Model 1 and the regression forecast combinations, using the MM and RQ methods. For women, the best methodologies were Model 1 and regression prediction combinations by the RQ method.

Are Combined Tourism Forecasts Better at Minimizing Forecasting Errors?

This study, which was contracted by the European Commission and is geared towards easy replicability by practitioners, compares the accuracy of individual and combined approaches to forecasting tourism demand for the total European Union. The evaluation of the forecasting accuracies was performed recursively (i.e., based on expanding estimation windows) for eight quarterly periods spanning two years in order to check the stability of the outcomes during a changing macroeconomic environment. The study sample includes Eurostat data from January 2005 until August 2017, and out of sample forecasts were calculated for the last two years for three and six months ahead. The analysis of the out-of-sample forecasts for arrivals and overnights showed that forecast combinations taking the historical forecasting performance of individual approaches such as Autoregressive Integrated Moving Average (ARIMA) models, REGARIMA models with different trend variables, and Error Trend Seasonal (ETS) models into account deliver the best results.

FORECASTING MULTIPLE FUNCTIONAL TIME SERIES IN A GROUP STRUCTURE: AN APPLICATION TO MORTALITY

When modelling subnational mortality rates, we should consider three features: (1) how to incorporate any possible correlation among subpopulations to potentially improve forecast accuracy through multi-population joint modelling; (2) how to reconcile subnational mortality forecasts so that they aggregate adequately across various levels of a group structure; (3) among the forecast reconciliation methods, how to combine their forecasts to achieve improved forecast accuracy. To address these issues, we introduce an extension of grouped univariate functional time-series method. We first consider a multivariate functional time-series method to jointly forecast multiple related series. We then evaluate the impact and benefit of using forecast combinations among the forecast reconciliation methods. Using the Japanese regional age-specific mortality rates, we investigate 1–15-step-ahead point and interval forecast accuracies of our proposed extension and make recommendations.