Journal of Forecasting
0277-6693
1099-131X
Anh Quốc
Cơ quản chủ quản: WILEY , John Wiley and Sons Ltd
Lĩnh vực:
Computer Science ApplicationsManagement Science and Operations ResearchEconomics and EconometricsModeling and SimulationStatistics, Probability and UncertaintyStrategy and Management
Phân tích ảnh hưởng
Thông tin về tạp chí
The Journal of Forecasting is an international journal that publishes refereed papers on forecasting. It is multidisciplinary, welcoming papers dealing with any aspect of forecasting: theoretical, practical, computational and methodological. A broad interpretation of the topic is taken with approaches from various subject areas, such as statistics, economics, psychology, systems engineering and social sciences, all encouraged. Furthermore, the Journal welcomes a wide diversity of applications in such fields as business, government, technology and the environment. Of particular interest are papers dealing with modelling issues and the relationship of forecasting systems to decision-making processes.
Các bài báo tiêu biểu
Adaptive modelling and forecasting of offshore wind power fluctuations with Markov‐switching autoregressive models ABSTRACT Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime‐switching behaviour with an approach relying on Markov‐switching autoregressive (MSAR) models. An appropriate parameterization of the model coefficients is introduced, along with an adaptive estimation method allowing accommodation of long‐term variations in the process characteristics. The objective criterion to be recursively optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one‐step‐ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill. Copyright © 2010 John Wiley & Sons, Ltd.
Tập 31 Số 4 - Trang 281-313 - 2012
On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model Recent multivariate extensions of the popular heterogeneous autoregressive model (HAR) for realized volatility leave substantial information unmodelled in residuals. We propose to employ a system of seemingly unrelated regressions to model and forecast a realized covariance matrix to capture this information. We find that the newly proposed generalized heterogeneous autoregressive (GHAR) model outperforms competing approaches in terms of economic gains, providing better mean–variance trade‐off, while, in terms of statistical precision, GHAR is not substantially dominated by any other model. Our results provide a comprehensive comparison of the performance when realized covariance, subsampled realized covariance and multivariate realized kernel estimators are used. We study the contribution of the estimators across different sampling frequencies, and show that the multivariate realized kernel and subsampled realized covariance estimators deliver further gains compared to realized covariance estimated on a 5‐minute frequency. In order to show economic and statistical gains, a portfolio of various sizes is used. Copyright © 2016 John Wiley & Sons, Ltd.
Tập 36 Số 2 - Trang 181-206 - 2017
Prediction uncertainty in an ecological model of the oosterschelde estuary Abstract A storm surge barrier was constructed in 1987 in the Oosterschelde estuary in the south‐western delta of Holland to provide protection from flooding, while largely maintaining the tidal characteristics of the estuary. Despite efforts to minimize the hydraulic changes resulting from the barrage, it was expected that exchange with the North Sea, suspended sediment concentration and nutrient loads would decrease considerably. A model of the nutrients, algae and bottom organisms (mainly cockles and mussels) was developed to predict possible changes in the availability of food for these organisms. Although the model is based on standard constructs of ecology and hydraulics, many of its parameters are known with but low accuracy, being expressed as a range of possible values only. Running the model with all possible values of the parameters gives rise to a fairly wide range of model output responses. The calibration procedure used herein does not seek a single optimal value for the parameters but a decrease in the parameter range and thus a reduction in model prediction uncertainty. The field data available for calibration of the model are weighted according to their relationship with the model's objective, i.e. to predict food availability for shellfish. Despite the considerable physical changes resulting from the barrier food availability for shellfish is predicted to remain largely unchanged, due to the compensating effects of several other accompanying changes. There appears to be room for the extension of mussel culture, but at an increased risk of adverse conditions arising.
Tập 10 Số 1-2 - Trang 191-209 - 1991
The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach ABSTRACT This paper applies the GARCH‐MIDAS (mixed data sampling) model to examine whether information contained in macroeconomic variables can help to predict short‐term and long‐term components of the return variance. A principal component analysis is used to incorporate the information contained in different variables. Our results show that including low‐frequency macroeconomic information in the GARCH‐MIDAS model improves the prediction ability of the model, particularly for the long‐term variance component. Moreover, the GARCH‐MIDAS model augmented with the first principal component outperforms all other specifications, indicating that the constructed principal component can be considered as a good proxy of the business cycle. Copyright © 2013 John Wiley & Sons, Ltd.
Tập 32 Số 7 - Trang 600-612 - 2013
Forecasting Performance of Nonlinear Models for Intraday Stock Returns ABSTRACT We studied the predictability of intraday stock market returns using both linear and nonlinear time series models. For the S&P 500 index we compared simple autoregressive and random walk linear models with a range of nonlinear models, including smooth transition, Markov switching, artificial neural network, nonparametric kernel regression and support vector machine models for horizons of 5, 10, 20, 30 and 60 minutes. The empirical results indicate that nonlinear models outperformed linear models on the basis of both statistical and economic criteria. Specifically, although return serial correlation receded by around 10 minutes, return predictability still persisted for up to 60 minutes according to nonlinear models, even though profitability decreases as time elapses. More flexible nonlinear models such as support vector machines and artificial neural network did not clearly outperform other nonlinear models. Copyright © 2011 John Wiley & Sons, Ltd.
Tập 31 Số 2 - Trang 172-188 - 2012
From forecasting to foresight processes—new participative foresight activities in Germany Abstract The definitions of forecasting vary to a certain extent, but they all have the view into the future in common. The future is unknown, but the broad, general directions can be guessed at and reasonably dealt with. Foresight goes further than forecasting, including aspects of networking and the preparation of decisions concerning the future. This is one reason why, in the 1990s, when foresight focused attention on a national scale in many countries, the wording also changed from forecasting to foresight. Foresight not only looks into the future by using all instruments of futures research, but includes utilizing implementations for the present. What does a result of a futures study mean for the present? Foresight is not planning, but foresight results provide ‘information’ about the future and are therefore one step in the planning and preparation of decisions. In this paper, some of the differences are described in a straightforward manner and demonstrated in the light of the German foresight process ‘Futur’. Copyright © 2003 John Wiley & Sons, Ltd.
Tập 22 Số 2-3 - Trang 93-111 - 2003
Estimation procedures for structural time series models Abstract A univariate structural time series model based on the traditional decomposition into trend, seasonal and irregular components is defined. A number of methods of computing maximum likelihood estimators are then considered. These include direct maximization of various time domain likelihood function. The asymptotic properties of the estimators are given and a comparison between the various methods in terms of computational efficiency and accuracy is made. The methods are then extended to models with explanatory variables.
Tập 9 Số 2 - Trang 89-108 - 1990
Improved methods of combining forecasts Abstract It is well known that a linear combination of forecasts can outperform individual forecasts. The common practice, however, is to obtain a weighted average of forecasts, with the weights adding up to unity. This paper considers three alternative approaches to obtaining linear combinations. It is shown that the best method is to add a constant term and not to constrain the weights to add to unity. These methods are tested with data on forecasts of quarterly hog prices, both within and out of sample. It is demonstrated that the optimum method proposed here is superior to the common practice of letting the weights add up to one.
Tập 3 Số 2 - Trang 197-204 - 1984
Why do EMD‐based methods improve prediction? A multiscale complexity perspective Abstract Empirical mode decomposition (EMD)‐based ensemble methods have become increasingly popular in the research field of forecasting, substantially enhancing prediction accuracy. The key factor in this type of method is the multiscale decomposition that immensely mitigates modeling complexity. Accordingly, this study probes this factor and makes further innovations from a new perspective of multiscale complexity. In particular, this study quantitatively investigates the relationship between the decomposition performance and prediction accuracy, thereby developing (1) a novel multiscale complexity measurement (for evaluating multiscale decomposition), (2) a novel optimized EMD (OEMD) (considering multiscale complexity), and (3) a novel OEMD‐based forecasting methodology (using the proposed OEMD in multiscale analysis). With crude oil and natural gas prices as samples, the empirical study statistically indicates that the forecasting capability of EMD‐based methods is highly reliant on the decomposition performance; accordingly, the proposed OEMD‐based methods considering multiscale complexity significantly outperform the benchmarks based on typical EMDs in prediction accuracy.
Tập 38 Số 7 - Trang 714-731 - 2019
Using the yield curve to forecast economic growth Abstract This paper finds the yield curve to have a well‐performing ability to forecast the real gross domestic product growth in the USA, compared to professional forecasters and time series models. Past studies have different arguments concerning growth lags, structural breaks, and ultimately the ability of the yield curve to forecast economic growth. This paper finds such results to be dependent on the estimation and forecasting techniques employed. By allowing various interest rates to act as explanatory variables and various window sizes for the out‐of‐sample forecasts, significant forecasts from many window sizes can be found. These seemingly good forecasts may face issues, including persistent forecasting errors. However, by using statistical learning algorithms, such issues can be cured to some extent. The overall result suggests, by scientifically deciding the window sizes, interest rate data, and learning algorithms, many outperforming forecasts can be produced for all lags from one quarter to 3 years, although some may be worse than the others due to the irreducible noise of the data.
Tập 39 Số 7 - Trang 1057-1080 - 2020