Journal of Forecasting最新文献

As the international energy market pays more and more attention to the development of clean energy, wind power is gradually attracting the attention of various countries. Wind power is a sustainable and environmentally friendly resource of energy. However, it is unstable. Therefore, it is important to develop algorithms for its prediction. In this paper, we apply a recently developed algorithm that effectively combines the tensor train decomposition with the higher order dynamic mode decomposition (TT-HODMD). The dynamic mode decomposition (DMD) is a data-driven technique that does not need a prior mathematical model. It is based on the measurement data or time slots. As demonstrated, for prediction it is important to use the higher order DMD (HODMD). In turn, HODMD might lead to very large scale arrays that are sparse. The tensor train decomposition provides a highly efficient way to work with such arrays. It is demonstrated that the combined TT-HODMD algorithm is capable of providing quite accurate prediction of wind power for months ahead.

Accurate prior information of passenger flow demand on high-speed railway is of great significance for the operation and the management of transportation systems. Various factors in modern social life have caused uncertainty at demand. Recently, individuals are increasingly depending on the online search results when choosing among different transportation modes, services, and destinations, which provide important basic information for forecasting the travel demand. This study employs Baidu search index to assist in capturing volatility of high-speed railway passenger demands, offering insights into the travel inclinations and travelers' actions. Furthermore, we have given more in-depth attention and analysis to their residual term accounting for the random nature caused by various factors. To this end, a sophisticated deep analysis mechanism based on data decomposition has been devised to extract and analyze the valuable information concealed within the residuals, so as to enhance the comprehension of the variability inherent in the high-speed railway passenger flow. Meanwhile, an error correction strategy is implemented for all residual terms to improve further their forecasting accuracy. Experimental results from two real-world datasets demonstrate the effectiveness and robustness of the developed hybrid approach across several popular evaluation indicators. Therefore, this study can function as a reliable instrument, provide sensible data-driven guidance for resource allocation and make scientific decisions in the railway industry.

From the cross-market perspective, this paper investigates crude oil volatility index (OVX) forecasts by proposing a hybrid method, which combines the data-driven SVR technique and parametric models. In terms of parametric models, we utilize GARCH-type models with jumps, and the forecasting effects of five non-parametric jumps (including interday and intraday jump tests) of stock market are also explored. Empirical results show that our approach can substantially increase forecasting accuracy. In addition, the model confidence set test and robust test reaffirm the superiority of the novel hybrid method. From the assessment of economic significance, the advantages of the hybrid method for volatility index forecasting are further confirmed. All these findings imply that jumps of stock market can be helpful in forecasting OVX, especially after the introduction of the hybrid method. Our work can certainly provide a new insight for volatility forecasting and cross-market research.

In this paper we develop nowcasting models for the Pays-de-la-Loire's jobseekers, a dynamic French regional economy. We ask whether these regional nowcasts are more accurate by only using the regional data or by combining the national and regional data. For this purpose, we use penalized regressions, random forest, and dynamic factor models as well as dimension reduction approaches. The best nowcasting performance is provided by the DFM estimated on the regional and regional-national databases as well as the Elastic-Net model with a prior screening step for which the national data are the most frequently selected data. For the latter, it appears that the Change in foreign orders in the industry sector, the OECD Composite leading indicator, and the BdF Business sentiment indicator are among the major predictors.

As an efficacious solution to remedying the imbalance of medical resources, the online medical platform has burgeoned expeditiously. Apt allotment of medical resources on the medical platform can facilitate patients in reasonably selecting physicians and time slots, coordinating doctors' clinical arrangements, and generating precise projections of medical platform service volume to enhance patient satisfaction and alleviate physicians' workload. To this end, grounded in the data-driven method, this paper assembles an exhaustive feature set encompassing hospital features, physician features, and patient features. Through feature selection, appropriate features are screened, and machine learning algorithms are leveraged to accurately forecast doctors' online consultation volume. Subsequently, to glean the influence relationship between online medical services and offline medical services, this paper introduces features of offline medical services such as hospital registration volume and regional gross domestic product (GDP) to solve the prediction of offline medical service volume using online medical information. The findings signify that online data feature prediction can pinpoint superior machine learning models for online medical platform service volume (with the optimal accuracy up to 96.89%). Online features exert a positive effect on predicting offline medical service volume, but the accuracy declines to some degree (the optimal accuracy is 73%). Physicians with favorable reputations on the online platform are more susceptible to attain higher offline appointment volumes when online consultation volume is a vital feature impacting offline appointment volume.

Exchange rate modeling has always fascinated researchers because of its complex macroeconomic dynamics. This study documents the exchange rate dynamics of major emerging economies after accounting for their macroeconomic cycles and explores the Bayesian Vector Error Correction Model (VECM) Markov Regime switching model, which uses time-varying transition probabilities. The main objective is to study the exchange rate dynamics of Brazil, Russia, India, China, and South Africa (BRICS) vis-à-vis the US dollar. The Bayesian setup uses two hierarchal shrinkage priors, the normal-gamma (NG) prior and the Litterman prior, for parameters' estimation. These shrinkage priors allow for a more comprehensive assessment of the regime-specific coefficients. The model performed well in differentiating between the two regimes for all currencies. The Russian ruble was identified to be the most depreciated currency, whereas the African Rand was the most appreciated. The evaluation of model features revealed that many regime-specific coefficients differed significantly from their common mean. A forecasting exercise was then performed for the out-of-sample period to assess the model's performance. A significant improvement was observed over the basic random walk (RW) model and the linear Bayesian vector autoregression (BVAR) model.

In this paper, we show that traditional comparisons of mean squared prediction error (MSPE) between two competing forecasts may be highly controversial. This is so because when some specific conditions of efficiency are not met, the forecast displaying the lowest MSPE will also display the lowest correlation with the target variable. Given that violations of efficiency are usual in the forecasting literature, this opposite behavior in terms of accuracy and correlation with the target variable may be a fairly common empirical finding that we label here as “the MSPE paradox.” We characterize “paradox zones” in terms of differences in correlation with the target variable and conduct some simple simulations to show that these zones may be non-empty sets. Finally, we illustrate the relevance of the paradox with a few empirical applications.

Recently, there is a new stream of mortality forecasting research using the vector autoregressive model with different sparse model specifications. They have been shown to be able to overcome some of the limitations of the more traditional factor models such as the Lee–Carter model. In this paper, we propose a more generalized systematic vector autoregressive framework for modeling and forecasting mortality. Under this framework, we progressively increase the sophistication of the diagonal parameters in the autoregressive matrix and formulate a range of model structures in a systematic fashion. They offer much flexibility for capturing the mortality patterns of different populations. The resulting models produce age coherent forecasts, and their parameters are reasonably interpretable for modelers, demographers, and industry practitioners. Using the mortality data of Australia, Japan, New Zealand, and Taiwan, we demonstrate that the proposed approach generates appropriate forecasts of mortality rates and life expectancies and produces very good performance in the fitting and out-of-sample analysis.

This paper proposes an asymmetric grouping estimator for forecasting per capita carbon emissions for a country panel. The estimator relies on the observation that a bias-variance pooling trade-off in potentially heterogeneous panel data may be different across countries. For a specific country, cross validation is used to determine the optimal country-specific grouping. A simulated annealing algorithm deals with the combinatorial problem of group selection in large cross sections. A Monte Carlo study shows that in case of heterogenous parameters, the asymmetric grouping estimators outperforms symmetric grouping approaches and forecasting based on individual estimates. Only in the case where the signal is very weak, pooling all countries leads to better forecasting performance. Similar results are found when forecasting carbon emission. The asymmetric grouping estimator leads to more pooling than a symmetric approach. Being on the same continent increases the probability of pooling, and African countries seem to benefit most from using asymmetric grouping and European countries least.

This paper uses information at the daily, monthly, and quarterly frequencies to construct a daily World Economic Gauge (WEG). We postulate a mixed-frequency dynamic factor model to extract data observable at different frequencies in order to track the health of the global economy. We show that the WEG offers a reliable basis for tracking economic activity during key events such as COVID-19 and the Global Financial Crisis. Moreover, the WEG is shown to contain leading information about the output growth of the OECD, G7, NAFTA, European Union, and euro areas, in addition to the output growth of 42 individual countries.