Search results

With the rise in wine consumption, accurate wine price forecasts have significantly impacted restaurant and hotel purchasing decisions and inventory management. This study aims to develop a precise and effective wine price point and interval forecasting model.

The proposed forecast model uses an improved hybrid kernel extreme learning machine with an attention mechanism and a multi-objective swarm intelligent optimization algorithm to produce more accurate price estimates. To the best of the authors’ knowledge, this is the first attempt at applying artificial intelligence techniques to improve wine price prediction. Additionally, an effective method for predicting price intervals was constructed by leveraging the characteristics of the error distribution. This approach facilitates quantifying the uncertainty of wine price fluctuations, thus rendering decision-making by relevant practitioners more reliable and controllable.

The empirical findings indicated that the proposed forecast model provides accurate wine price predictions and reliable uncertainty analysis results. Compared with the benchmark models, the proposed model exhibited superiority in both one-step- and multi-step-ahead forecasts. Meanwhile, the model provides new evidence from artificial intelligence to explain wine prices and understand their driving factors.

This study is a pioneering attempt to evaluate the applicability and effectiveness of advanced artificial intelligence techniques in wine price forecasts. The proposed forecast model not only provides useful options for wine price forecasting but also introduces an innovative addition to existing forecasting research methods and literature.

This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network.

Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models.

The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels.

This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR.

This study produced a reliable, accurate forecasting model considering risk and competitor behavior.

This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times.

This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.

Integrating artificial intelligence and quantitative investment has given birth to various agricultural futures price prediction models suitable for nonlinear and non-stationary data. However, traditional models have limitations in testing the spatial transmission relationship in time series, and the actual prediction effect is restricted by the inability to obtain the prices of other variable factors in the future.

To explore the impact of spatiotemporal factors on agricultural prices and achieve the best prediction effect, the authors innovatively propose a price prediction method for China's soybean and palm oil futures prices. First, an improved Granger Causality Test was adopted to explore the spatial transmission relationship in the data; second, the Seasonal and Trend decomposition using Loess model (STL) was employed to decompose the price; then, the Apriori algorithm was applied to test the time spillover effect between data, and CRITIC was used to extract essential features; finally, the N-Beats model was selected as the prediction model for futures prices.

Using the Apriori and STL algorithms, the authors found a spillover effect in agricultural prices, and past trends and seasonal data will impact future prices. Using the improved Granger causality test method to analyze the unidirectional causality relationship between the prices, the authors obtained a spatial effect among the agricultural product prices. By comparison, the N-Beats model based on the spatiotemporal factors shows excellent prediction effects on different prices.

This paper addressed the problem that traditional models can only predict the current prices of different agricultural products on the same date, and traditional spatial models cannot test the characteristics of time series. This result is beneficial to the sustainable development of agriculture and provides necessary numerical and technical support to ensure national agricultural security.

This paper aims to investigate and formulate several business models (BM) for various energy communities (EC) members: prosumers, storage facilities, electric vehicle (EV) charging stations, aggregators and local markets.

One of the flexibility drivers is triggered by avoiding the cost and maximizing value that consists of delivering a service such as increasing generation or reducing consumption when it is valued most. The transition to greener economies led to the emergence of aggregators that aggregate bits of flexibility and handle the interest of their providers, e.g. small entities such as consumers, prosumers and other small service providers. On one hand, the research method consists of formulating six BM and implementing a BM that includes several consumers and an aggregator, namely, scheduling the household electricity consumption (downstream) and using flexibility to obtain revenue or avoid the cost. This is usually performed by reducing or shifting the consumption from peak to off-peak hours when the energy is cheaper. Thus, the role of aggregators in EC is significant as they intermediate small-scale energy threads and large entities' requirements, such as grid operators or retailers. On the other hand, in the proposed BM, the aggregators' strategy (upstream) will be to minimize the cost of electricity procurement using consumers’ flexibility. They set up markets to buy flexibility that is valued as long as their costs are reduced.

Interesting insights are revealed, such as when the flexibility price doubles, the deficit coverage increases from 62% to 91% and both parties, consumers and retailers obtain financial benefits from the local market.

One of the limitations of using the potential of flexibility is related to the high costs that are necessary to implement direct load control. Another issue is related to the data privacy aspects related to the breakdown of electricity consumption. Furthermore, data availability for scientific research is limited. However, this study expects that new BM for various EC members will emerge in the future largely depending on Information Communications and Technology developments.

An implementation of a local flexibility market (LFM) using 114 apartments with flexible loads is proposed, demonstrating the gains obtained from trading flexibility. For LFM simulation, this study considers exemplifying a BM using 114 apartments located in a multi-apartment building representing a small urban EC situated in the New England region in North America. Open data recorded in 2016 is provided by UMassTraceRepository.

As a novelty, six BM are proposed considering a bottom-up approach and including various EC members.

Coal is a critical global energy source, and fluctuations in its price significantly impact related enterprises' profitability. This study aims to develop a robust model for predicting the coal price index to enhance coal purchase strategies for coal-consuming enterprises and provide crucial information for global carbon emission reduction.

The proposed coal price forecasting system combines data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. It addresses the challenge of merging low-resolution and high-resolution data by adaptively combining both types of data and filling in missing gaps through interpolation for internal missing data and self-supervision for initiate/terminal missing data. The system employs self-supervised learning to complete the filling of complex missing data.

The ensemble model, which combines long short-term memory, XGBoost and support vector regression, demonstrated the best prediction performance among the tested models. It exhibited superior accuracy and stability across multiple indices in two datasets, namely the Bohai-Rim steam-coal price index and coal daily settlement price.

The proposed coal price forecasting system stands out as it integrates data decomposition, semi-supervised feature engineering, ensemble learning and deep learning. Moreover, the system pioneers the use of self-supervised learning for filling in complex missing data, contributing to its originality and effectiveness.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

The Chinese housing market has gone through rapid growth during the past decade, and house price forecasting has evolved to be a significant issue that draws enormous attention from investors, policy makers and researchers. This study investigates neural networks for composite property price index forecasting from ten major Chinese cities for the period of July 2005–April 2021.

The goal is to build simple and accurate neural network models that contribute to pure technical forecasts of composite property prices. To facilitate the analysis, the authors consider different model settings across algorithms, delays, hidden neurons and data spitting ratios.

The authors arrive at a pretty simple neural network with six delays and three hidden neurons, which generates rather stable performance of average relative root mean square errors across the ten cities below 1% for the training, validation and testing phases.

Results here could be utilized on a standalone basis or combined with fundamental forecasts to help form perspectives of composite property price trends and conduct policy analysis.

This paper aims to propose an improved version of portfolio optimization model through the prediction of the future behavior of stock returns using a combined wavelet-based long short-term memory (LSTM).

First, data are gathered and divided into two parts, namely, “past data” and “real data.” In the second stage, the wavelet transform is proposed to decompose the stock closing price time series into a set of coefficients. The derived coefficients are taken as an input to the LSTM model to predict the stock closing price time series and the “future data” is created. In the third stage, the mean-variance portfolio optimization problem (MVPOP) has iteratively been run using the “past,” “future” and “real” data sets. The epsilon-constraint method is adapted to generate the Pareto front for all three runes of MVPOP.

The real daily stock closing price time series of six stocks from the FTSE 100 between January 1, 2000, and December 30, 2020, is used to check the applicability and efficacy of the proposed approach. The comparisons of “future,” “past” and “real” Pareto fronts showed that the “future” Pareto front is closer to the “real” Pareto front. This demonstrates the efficacy and applicability of proposed approach.

Most of the classic Markowitz-based portfolio optimization models used past information to estimate the associated parameters of the stocks. This study revealed that the prediction of the future behavior of stock returns using a combined wavelet-based LSTM improved the performance of the portfolio.

The purpose of the study is to investigate the impact of implementing anti-dumping duties on imported Australian wine to China in the short- and long-run, respectively.

First, the Difference-in-Differences (DID) method is used in this study to evaluate the short-run causal effect of implementing anti-dumping duties on imported Australian wine to China. Second, a Bayesian ensemble method is used to predict 2023–2025 wine exports from Australia to China. The disparity between the forecasts and counterfactual prediction which assumes no anti-dumping duties represents the accumulated impact of the anti-dumping duties in the long run.

The anti-dumping duties resulted in a significant decline in red and rose, white and sparkling wine exports to China by 92.59%, 99.06% and 90.06%, respectively, in 2021. In the long run, wine exports to China are projected to continue this downward trend, with an average annual growth rate of −21.92%, −38.90% and −9.54% for the three types of wine, respectively. In contrast, the counterfactual prediction indicates an increase of 3.20%, 20.37% and 4.55% for the respective categories. Consequently, the policy intervention is expected to result in a decrease of 96.11%, 93.15% and 84.11% in red and rose, white and sparkling wine exports to China from 2021 to 2025.

The originality of this study lies in the creation of an economic paradigm for assessing policy impacts within the realm of wine economics. Methodologically, it also represents the pioneering application of the DID and Bayesian ensemble forecasting methods within the field of wine economics.

This paper aims to develop a house price forecasting model to investigate the impact of neighbourhood effect on property value.

Multi-level modelling (MLM) method is used to develop the house price forecasting models. The neighbourhood effects, that is, socio-economic conditions that exist in various locations, are included in this study. Data from the local government area in Greater Sydney, Australia, has been collected to test the developed model.

Results show that the multi-level models can account for the neighbourhood effects and provide accurate forecasting results.

It is believed that the impacts on specific households may be different because of the price differences in various geographic areas. The “neighbourhood” is an important consideration in housing purchase decisions.

While increasing housing supply provisions to match the housing demand, governments may consider improving the quality of neighbourhood conditions such as transportation, surrounding environment and public space security.

The demand and supply of housing in different locations have not behaved uniformly over time, that is, they demonstrate spatial heterogeneity. The use of MLM extends the standard hedonic model to incorporate physical characteristics and socio-economic variables to estimate dwelling prices.