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1 – 10 of over 60000Pierre Rostan and Alexandra Rostan
The purpose of this paper is to present forecasts of fossil fuels prices until 2030 with spectral analysis to provide a clearer picture of this energy sector.
Abstract
Purpose
The purpose of this paper is to present forecasts of fossil fuels prices until 2030 with spectral analysis to provide a clearer picture of this energy sector.
Design/methodology/approach
Fossil fuels prices time series are decomposed in simpler signals called approximations and details in the framework of the one-dimensional discrete wavelet analysis. The simplified signals are recomposed after Burg extension.
Findings
In 2019-2030 average price forecasts of: West Texas intermediate (WTI) oil ($58.67) is above its 1986-2030 long-term mean of $47.83; and coal ($81.01) is above its 1980-2030 long-term mean of $60.98. On the contrary, 2019-2030 average of price forecasts of: Henry Hub natural gas ($3.66) is below its 1997-2030 long-term mean of $4; heating oil ($0.64) is below its 1986-2030 long-term mean of $1.16; propane ($0.26) is below its 1992-2030 long-term mean of $0.66; and regular gasoline ($1.45) is below its 2003-2030 long-term mean of $1.87.
Originality/value
Fossil fuels prices projections may relieve participants of WTI oil and coal markets but worry participants of Henry Hub, heating oil, propane and regular gasoline markets including countries whose economy is tied to energy prices.
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Marc Gürtler and Thomas Paulsen
Empirical publications on the time series modeling and forecasting of electricity prices vary widely regarding the conditions, and the findings make it difficult to generalize…
Abstract
Purpose
Empirical publications on the time series modeling and forecasting of electricity prices vary widely regarding the conditions, and the findings make it difficult to generalize results. Against this background, it is surprising that there is a lack of statistics-based literature reviews on the forecasting performance when comparing different models. The purpose of the present study is to fill this gap.
Design/methodology/approach
The authors conduct a comprehensive literature analysis from 2000 to 2015, covering 86 empirical studies on the time series modeling and forecasting of electricity spot prices. Various statistics are presented to characterize the empirical literature on electricity spot price modeling, and the forecasting performance of several model types and modifications is analyzed. The key issue of this study is to offer a comparison between different model types and modeling conditions regarding their forecasting performance, which is referred to as a quasi-meta-analysis, i.e. the analysis of analyses to achieve more general findings independent of the circumstances of single studies.
Findings
The authors find evidence that generalized autoregressive conditional heteroscedasticity models outperform their autoregressive–moving-average counterparts and that the consideration of explanatory variables improves forecasts.
Originality/value
To the best knowledge of the authors, this paper is the first to apply the methodology of meta-analyses in a literature review of the empirical forecasting literature on electricity spot markets.
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Keywords
Peter Kačmáry, Peter Bindzár, Jakub Kovalčík and Marek Ondov
The purpose of this paper is to apply and verify Fourier series analysis in combination with non-linear regression as a tool of forecasting and planning of inputs in the logistics…
Abstract
Purpose
The purpose of this paper is to apply and verify Fourier series analysis in combination with non-linear regression as a tool of forecasting and planning of inputs in the logistics process of a retail chain store.
Design/methodology/approach
For many popular products, a significant effect of seasonality of sales is expected; therefore, the method of Fourier series was chosen as one of the main forecast calculation techniques. However, the use of this method directly for forecasting sales has a limitation in the form of a complete reconstruction of the shape of the curve from of the given monitored time. Thus, the forecast is based only on the significant harmonic components from the Fourier series analysis that will participate in forecast forming. In addition, to respect the trend of series, the results of Fourier series analysis are combined with the non-linear regression.
Findings
The results showed that the number of significant harmonic components from the Fourier series analysis is suitable to reflect the future behaviour of the sale in standard market conditions. Forecasting of the sale and accurate purchase planning of goods has a positive effect on reducing the waste of unsold products after their shelf and on increasing of a customer satisfaction.
Research limitations/implications
This study has an application in a certain period of time (relatively calm behaviour of the food market) and only for a certain region. Therefore, it is not possible to generalize these results as the behaviour of consumers, e.g. within the state. It will also be interesting to monitor and forecast sales of other food items.
Practical implications
This provides a practical and relatively simple tool for implementing or improving the process of forecasting seasonally dependent products in the food industry.
Originality/value
This study shows the possibility of forecast that is based on adding the significant harmonic components from the Fourier series analysis to form forecast with the non-linear regression.
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Doris Chenguang Wu, Haiyan Song and Shujie Shen
The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging…
Abstract
Purpose
The purpose of this paper is to review recent studies published from 2007 to 2015 on tourism and hotel demand modeling and forecasting with a view to identifying the emerging topics and methods studied and to pointing future research directions in the field.
Design/methodology/approach
Articles on tourism and hotel demand modeling and forecasting published mostly in both science citation index and social sciences citation index journals were identified and analyzed.
Findings
This review finds that the studies focused on hotel demand are relatively less than those on tourism demand. It is also observed that more and more studies have moved away from the aggregate tourism demand analysis, whereas disaggregate markets and niche products have attracted increasing attention. Some studies have gone beyond neoclassical economic theory to seek additional explanations of the dynamics of tourism and hotel demand, such as environmental factors, tourist online behavior and consumer confidence indicators, among others. More sophisticated techniques such as nonlinear smooth transition regression, mixed-frequency modeling technique and nonparametric singular spectrum analysis have also been introduced to this research area.
Research limitations/implications
The main limitation of this review is that the articles included in this study only cover the English literature. Future review of this kind should also include articles published in other languages. The review provides a useful guide for researchers who are interested in future research on tourism and hotel demand modeling and forecasting.
Practical implications
This review provides important suggestions and recommendations for improving the efficiency of tourism and hospitality management practices.
Originality/value
The value of this review is that it identifies the current trends in tourism and hotel demand modeling and forecasting research and points out future research directions.
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The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management…
Abstract
Purpose
The purpose of this article is to present an overview of the area of strategic forecasting and its research directions and to put forward some ideas for improving management decisions.
Design/methodology/approach
This article is conceptual but also informed by the author’s long contact and collaboration with various business firms. It starts by presenting an overview of the area and argues that the area is as much a way of thinking as a toolbox of theories and methodologies. It then spells out a number of research directions and ideas for management.
Findings
Strategic forecasting is seen as a rebirth of long range planning, albeit with new methods and theories. Firms should make the building of strategic forecasting capability a priority.
Research limitations/implications
The article subdivides strategic forecasting into three research avenues and suggests avenues for further research efforts.
Practical implications
The article provides five examples of ideas that may enable managers to analyze and understand the future of their firm’s environment, thus improving investments in a wide variety of areas.
Originality/value
This article’s contribution is a relatively novel way of theorizing within a somewhat neglected area. It also suggests several new practical ideas that may improve management decisions.
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Keywords
The literature on financial statement analysis attempts to improve fundamental analysis and to identify market inefficiencies with respect to financial statement information.
Abstract
Purpose
The literature on financial statement analysis attempts to improve fundamental analysis and to identify market inefficiencies with respect to financial statement information.
Design/methodology/approach
In this paper, the author reviews the extant research on financial statement analysis.
Findings
The author then provides some preliminary evidence using Chinese data and offer suggestions for future research, with a focus on utilising unique features of the Chinese business environment as motivation.
Originality/value
The author notes that there has been no work that the author could locate specifically on Chinese FSA research. The unique business environment in China, relative to the US where the vast majority of this work has been conducted, should motivate any studies, especially given the author documents the robust finding in terms of the mean reversion in profitability.
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Keywords
Valery Gitis and Alexander Derendyaev
The purpose of this paper is to offer two Web-based platforms for systematic analysis of seismic processes. Both platforms are designed to analyze and forecast the state of the…
Abstract
Purpose
The purpose of this paper is to offer two Web-based platforms for systematic analysis of seismic processes. Both platforms are designed to analyze and forecast the state of the environment and, in particular, the level of seismic hazard. The first platform analyzes the fields representing the properties of the seismic process; the second platform forecasts strong earthquakes. Earthquake forecasting is based on a new one-class classification method.
Design/methodology/approach
The paper suggests an approach to systematic forecasting of earthquakes and examines the results of tests. This approach is based on a new method of machine learning, called the method of the minimum area of alarm. The method allows to construct a forecast rule that optimizes the probability of detecting target earthquakes in a learning sample set, provided that the area of the alarm zone does not exceed a predetermined one.
Findings
The paper presents two platforms alongside the method of analysis. It was shown that these platforms can be used for systematic analysis of seismic process. By testing of the earthquake forecasting method in several regions, it was shown that the method of the minimum area of alarm has satisfactory forecast quality.
Originality/value
The described technology has two advantages: simplicity of configuration for a new problem area and a combination of interactive easy analysis supported by intuitive operations and a simplified user interface with a detailed, comprehensive analysis of spatio-temporal processes intended for specialists. The method of the minimum area of alarm solves the problem of one-class classification. The method is original. It uses in training the precedents of anomalous objects and statistically takes into account normal objects.
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Murat Özemre and Ozgur Kabadurmus
The purpose of this paper is to present a novel framework for strategic decision making using Big Data Analytics (BDA) methodology.
Abstract
Purpose
The purpose of this paper is to present a novel framework for strategic decision making using Big Data Analytics (BDA) methodology.
Design/methodology/approach
In this study, two different machine learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN) are employed to forecast export volumes using an extensive amount of open trade data. The forecasted values are included in the Boston Consulting Group (BCG) Matrix to conduct strategic market analysis.
Findings
The proposed methodology is validated using a hypothetical case study of a Chinese company exporting refrigerators and freezers. The results show that the proposed methodology makes accurate trade forecasts and helps to conduct strategic market analysis effectively. Also, the RF performs better than the ANN in terms of forecast accuracy.
Research limitations/implications
This study presents only one case study to test the proposed methodology. In future studies, the validity of the proposed method can be further generalized in different product groups and countries.
Practical implications
In today’s highly competitive business environment, an effective strategic market analysis requires importers or exporters to make better predictions and strategic decisions. Using the proposed BDA based methodology, companies can effectively identify new business opportunities and adjust their strategic decisions accordingly.
Originality/value
This is the first study to present a holistic methodology for strategic market analysis using BDA. The proposed methodology accurately forecasts international trade volumes and facilitates the strategic decision-making process by providing future insights into global markets.
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Keywords
Treshani Perera, David Higgins and Woon-Weng Wong
Property market models have the overriding aim of predicting reasonable estimates of key dependent variables (demand, supply, rent, yield, vacancy and net absorption rate). These…
Abstract
Purpose
Property market models have the overriding aim of predicting reasonable estimates of key dependent variables (demand, supply, rent, yield, vacancy and net absorption rate). These can be based on independent drivers of core property and economic activities. Accurate predictions can only be conducted when ample quantitative data are available with fewer uncertainties. However, a broad-fronted social, technical and ecological evolution can throw up sudden, unexpected shocks that result in the econometric outputs sceptical to unknown risk factors. Therefore, the purpose of this paper is to evaluate Australian office market forecast accuracy and to determine whether the forecasts capture extreme downside risk events.
Design/methodology/approach
This study follows a quantitative research approach, using secondary data analysis to test the accuracy of economists’ forecasts. The forecast accuracy evaluation encompasses the measurement of economic and property forecasts under the following phases: testing for the forecast accuracy; analysing outliers of forecast errors; and testing of causal relationships. Forecast accuracy measurement incorporates scale independent metrics that include Theil’s U values (U1 and U2) and mean absolute scaled error. Inter-quartile range rule is used for the outlier analysis. To find the causal relationships among variables, the time series regression methodology is utilised, including multiple regression analysis and Granger causality developed under the vector auto regression (VAR).
Findings
The credibility of economic and property forecasts was questionable around the period of the Global Financial Crisis (GFC); a significant man-made Black Swan event. The forecast accuracy measurement highlighted rental movement and net absorption forecast errors as the critical inaccurate predictions. These key property variables are explained by historic information and independent economic variables. However, these do not explain the changes when error time series of the variables were concerned. According to VAR estimates, all property variables have a significant causality derived from the lagged values of Australian S&P/ASX 200 (ASX) forecast errors. Therefore, lagged ASX forecast errors could be used as a warning signal to adjust property forecasts.
Research limitations/implications
Secondary data were obtained from the premier Australian property markets: Canberra, Sydney, Brisbane, Adelaide, Melbourne and Perth. A limited ten-year timeframe (2001-2011) was used in the ex-post analysis for the comparison of economic and property variables. Forecasts ceased from 2011, due to the discontinuity of the Australian Financial Review quarterly survey of economists; the main source of economic forecast data.
Practical implications
The research strongly recommended naïve forecasts for the property variables, as an input determinant in each office market forecast equation. Further, lagged forecast errors in the ASX could be used as a warning signal for the successive property forecast errors. Hence, data adjustments can be made to ensure the accuracy of the Australian office market forecasts.
Originality/value
The paper highlights the critical inaccuracy of the Australian office market forecasts around the GFC. In an environment of increasing incidence of unknown events, these types of risk events should not be dismissed as statistical outliers in real estate modelling. As a proactive strategy to improve office market forecasts, lagged ASX forecast errors could be used as a warning signal. This causality was mirrored in rental movements and total vacancy forecast errors. The close interdependency between rents and vacancy rates in the forecasting process and the volatility in rental cash flows reflects on direct property investment and subsequently on the ASX, is therefore justified.
Details
Keywords
Sonali Shankar, Sushil Punia and P. Vigneswara Ilavarasan
Container throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of…
Abstract
Purpose
Container throughput forecasting plays a pivotal role in strategic, tactical and operational level decision-making. The determination and analysis of the influencing factors of container throughput are observed to enhance the predicting accuracy. Therefore, for effective port planning and management, this study employs a deep learning-based method to forecast the container throughput while considering the influence of economic, environmental and social factors on throughput forecasting.
Design/methodology/approach
A novel multivariate container throughput forecasting method is proposed using long short-term memory network (LSTM). The external factors influencing container throughput, delineated using triple bottom line, are considered as an input to the forecasting method. The principal component analysis (PCA) is employed to reduce the redundancy of the input variables. The container throughput data of the Port of Los Angeles (PLA) is considered for empirical analysis. The forecasting accuracy of the proposed method is measured via an error matrix. The accuracy of the results is further substantiated by the Diebold-Mariano statistical test.
Findings
The result of the proposed method is benchmarked with vector autoregression (VAR), autoregressive integrated moving average (ARIMAX) and LSTM. It is observed that the proposed method outperforms other counterpart methods. Though PCA was not an integral part of the forecasting process, it facilitated the prediction by means of “less data, more accuracy.”
Originality/value
A novel deep learning-based forecasting method is proposed to predict container throughput using a hybridized autoregressive integrated moving average with external factors model and long short-term memory network (ARIMAX-LSTM).
Details