Search

Effective and timely forecasting of daily tourism volume is an important topic for tourism practitioners and researchers, which can reduce waste and promote the sustainable development of tourism. Several studies are based on the decomposition-ensemble model to forecast the time series of high volatility in tourism volume, but ignore different forecasting methods suitable for different subseries. This study provides an adaptive decomposition-ensemble hybrid forecasting approach. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to effectively decompose the original time series into multiple relatively easy subseries, which reduces the complexity of the data. Secondly, sample entropy calculates the complexity of a sequence, and then adopts the elbow rule to adaptively divide them into different complex sets. Finally, multi-kernel extreme learning machine (KELM) models are used to forecast the components of different sets and integrate them. This hybrid approach makes full use of the advantages of different models, which enables effective use of data. The empirical results demonstrate that the approach can both produce results that are close to the actual values and be utilized as a strategy for forecasting daily tourism volume. [ABSTRACT FROM AUTHOR]

Existing research has shown that combination can effectively improve tourism forecasting accuracy compared with single model. However, the model uncertainty and structural instability in combination for out-of-sample tourism forecasting may influence the forecasting performance. This paper proposes a novel forecast combination approach based on time-varying jackknife model averaging (TVJMA), which can more efficiently handle structural changes and nonstationary trends in tourism data. Using Hong Kong tourism demand from five major tourism source regions as an empirical study, we investigate whether our proposed nonparametric TVJMA-based approach can improve tourism forecasting accuracy further. Empirical results show that the proposed TVJMA-based approach outperforms other competitors including single model and three combination methods in most cases. Findings indicate the outstanding performance of our method is robust to various forecasting horizons and different estimation periods. [ABSTRACT FROM AUTHOR]

A reliable and efficient forecasting system can be used to warn the general public against the increasing PM2.5 concentration. This paper proposes a novel AdaBoost‐ensemble technique based on a hybrid data preprocessing‐analysis strategy, with the following contributions: (i) a new decomposition strategy is proposed based on the hybrid data preprocessing‐analysis strategy, which combines the merits of two popular decomposition algorithms and has been proven to be a promising decomposition strategy; (ii) the long short‐term memory (LSTM), as a powerful deep learning forecasting algorithm, is applied to individually forecast the decomposed components, which can effectively capture the long‐short patterns of complex time series; and (iii) a novel AdaBoost‐LSTM ensemble technique is then developed to integrate the individual forecasting results into the final forecasting results, which provides significant improvement to the forecasting performance. To evaluate the proposed model, a comprehensive and scientific assessment system with several evaluation criteria, comparison models, and experiments is designed. The experimental results indicate that our developed hybrid model considerably surpasses the compared models in terms of forecasting precision and statistical testing and that its excellent forecasting performance can guide in developing effective control measures to decrease environmental contamination and prevent the health issues caused by a high PM2.5 concentration. [ABSTRACT FROM AUTHOR]

Practitioners and policy makers rely on accurate crude oil forecasting to avoid price risks and grasp investment opportunities, but the core of existing predictive models for such prices is based on point-valued inputs and outputs, which may suffer from informational loss of volatility. This paper addresses this issue by proposing a modified threshold autoregressive interval-valued models with interval-valued factors (MTARIX), as extended by Sun et al. [Threshold autoregressive models for interval-valued time series. J. Econom., 2018, 206, 414–446], to analyze and forecast interval-valued crude oil prices. In contrast to point-valued data methods, MTARIX models simultaneously capture nonlinear features in price trend and volatility, and this informational gain can produce more accurate forecasts. Several interval-valued factors and point-valued threshold variables are analyzed, including supply and demand, speculation, stock market, monetary market, technical factor, and search query data. Empirical results suggest that MTARIX models with appropriate threshold variables outperform other competing forecast models (ACIX, CR-SETARX, ARX, and VARX). The findings indicate that oil price range information is more valuable than oil price level information in forecasting crude oil prices. [ABSTRACT FROM AUTHOR]

Previous studies have shown that different market factors influence tourism demand at different timescales. Accordingly, we propose the decomposition ensemble learning approach to analyze impact of different market factors on tourism demand, and explore the potential advantages of the proposed method on forecasting tourism demand in Asia‐Pacific region. By decomposing tourist arrivals with noise‐assisted multivariate empirical mode decomposition, this study further explores the multiscale relationship between tourist destinations and major source countries. The empirical results show that decomposition ensemble approach performs significantly better than benchmarks in terms of the level forecasting accuracy and directional forecasting accuracy. [ABSTRACT FROM AUTHOR]

Search query data (SQD) can be helpful in predicting tourism demand by generating web search indexes. However, valuable nonlinear information in SQD may be neglected by researchers. To effectively capture the nonlinear information, we used kernel principal component analysis (KPCA) to extract web search indexes from SQD. Then, several models with KPCA-based web search indexes were developed for tourism demand forecasting. An empirical study was conducted with collected SQD and real data of tourist arrivals at Hong Kong. The results suggest that models with KPCA-based web search indexes are more accurate than other models because of the nonlinear data processing ability of the KPCA and demonstrate that KPCA-based web search indexes can be excellent predictors for tourism demand forecasting. [ABSTRACT FROM AUTHOR]

Crude oil prices are of vital importance for market participants and governments to make energy policies and decisions. In this paper, we apply a newly proposed autoregressive conditional interval (ACI) model to forecast crude oil prices. Compared with the existing point-based forecasting models, the interval-based ACI model can capture the dynamics of oil prices in both level and range of variation in a unified framework. Rich information contained in interval-valued observations can be simultaneously utilized, thus enhancing parameter estimation efficiency and model forecasting accuracy. In forecasting the monthly West Texas Intermediate (WTI) crude oil prices, we document that the ACI models outperform the popular point-based time series models. In particular, ACI models deliver better forecasts than univariate ARMA models and the vector error correction model (VECM). The gain of ACI models is found in out-of-sample monthly price interval forecasts as well as forecasts for point-valued highs, lows, and ranges. Compared with GARCH and conditional autoregressive range (CARR) models, ACI models are also superior in volatility (conditional variance) forecasts of oil prices. A trading strategy that makes use of the monthly high and low forecasts is further developed. This trading strategy generally yields more profitable trading returns under the ACI models than the point-based VECM. [ABSTRACT FROM AUTHOR]

In recent years, the body of literature on frequentist model averaging in econometrics has grown significantly. Most of this work focuses on models with different mean structures but leaves out the variance consideration. In this article, we consider a regression model with multiplicative heteroscedasticity and develop a model averaging method that combines maximum likelihood estimators of unknown parameters in both the mean and variance functions of the model. Our weight choice criterion is based on a minimization of a plug-in estimator of the model average estimator's squared prediction risk. We prove that the new estimator possesses an asymptotic optimality property. Our investigation of finite-sample performance by simulations demonstrates that the new estimator frequently exhibits very favorable properties compared with some existing heteroscedasticity-robust model average estimators. The model averaging method hedges against the selection of very bad models and serves as a remedy to variance function mis-specification, which often discourages practitioners from modeling heteroscedasticity altogether. The proposed model average estimator is applied to the analysis of two data sets on housing and economic growth. [ABSTRACT FROM AUTHOR]

A clustering-based nonlinear ensemble (CNE) learning approach is proposed in this paper to forecast exchange rates. In the proposed CNE learning approach: 1) a self-organizing map neural network is introduced to cluster the in-sample component forecasts; 2) kernel-based extreme learning machine is employed to calculate the in-sample ensemble weights for each cluster; and 3) the corresponding clusters’ in-sample ensemble weights are used for out-of-sample component forecasts to obtain the ensemble forecasts. To illustrate and verify the effectiveness of our proposed model, we test its directional and level forecasting accuracy using four major exchange rates. The out-of-sample forecasting performance results show that the proposed CNE learning approach consistently outperforms the component models and other ensemble learning approaches in terms of the directional forecasting accuracy and the level forecasting accuracy. [ABSTRACT FROM AUTHOR]

The coronavirus disease (COVID-19) pandemic has already caused enormous damage to the global economy and various industries worldwide, especially the tourism industry. In the post-pandemic era, accurate tourism demand recovery forecasting is a vital requirement for a thriving tourism industry. Therefore, this study mainly focuses on forecasting tourist arrivals from mainland China to Hong Kong. A new direction in tourism demand recovery forecasting employs multi-source heterogeneous data comprising economy-related variables, search query data, and online news data to motivate the tourism destination forecasting system. The experimental results confirm that incorporating multi-source heterogeneous data can substantially strengthen the forecasting accuracy. Specifically, mixed data sampling (MIDAS) models with different data frequencies outperformed the benchmark models. [ABSTRACT FROM AUTHOR]

Expert knowledge has been proved by substantial studies to be contributory to higher forecasting performance; meanwhile, its application is criticized and opposed by some groups for biases and inconsistency inherent in experts' subjective judgment. This paper proposes a new approach to improving forecasting performance, which takes advantage of expert knowledge by constructing a constraint equation rather than directly adjusting the predicted values by experts. For the comparison purpose, the proposed approach, together with several widely used models including ARIMA, BP-ANN and the judgment model (JM), is applied to forecasting the container throughput of Guangzhou Port, which is one of the most important ports of China. Forecasting performances of the above models are compared and the results clearly show superiority of the proposed approach over its rivals, which implies that expert knowledge will make positive contribution as long as it is used in a right way. [ABSTRACT FROM AUTHOR]

To enhance the prediction accuracy for crude oil price, a novel ensemble learning paradigm coupling complementary ensemble empirical mode decomposition (CEEMD) and extended extreme learning machine (EELM) is proposed. This novel method is actually an improved model under the effective "decomposition and ensemble" framework, especially for nonlinear, complex, and irregular data. In this proposed method, CEEMD, a current extension from the competitive decomposition family of empirical mode decomposition (EMD), is first applied to divide the original data (i.e., difficult task) into a number of components (i.e., relatively easy subtasks). Then, EELM, a recently developed, powerful, fast and stable intelligent learning technique, is implemented to predict all extracted components individually. Finally, these predicted results are aggregated into an ensemble result as the final prediction using simple addition ensemble method. With the crude oil spot prices of WTI and Brent as sample data, the empirical results demonstrate that the novel CEEMD-based EELM ensemble model statistically outperforms all listed benchmarks (including typical forecasting techniques and similar ensemble models with other decomposition and ensemble tools) in prediction accuracy. The results also indicate that the novel model can be used as a promising forecasting tool for complicated time series data with high volatility and irregularity. [ABSTRACT FROM AUTHOR]

This paper presents an integrated forecasting model based on the TEI@I methodology for forecasting demand for port logistics services – specifically, port container throughput. The model analyzes port logistics time series data and other information in several steps. In the first step, several econometric models are built to forecast the linear segment of port logistics time series. In the second step, a radial basis function neural network is developed to predict the nonlinear segment of the time series. In the third step, the event-study method and expert system techniques are applied to evaluate the effects of economic and other events that may impact demand for port logistics. In the final step, synthetic forecasting results are obtained, based on the integration of predictions from the above three steps. For an illustration, Hong Kong port's container throughput series is used as a case study. The empirical results show the effectiveness of the TEI@I integrated model for port logistics forecasting. [ABSTRACT FROM PUBLISHER]

Abstract: In this study, three hybrid approaches based on least squares support vector regression (LSSVR) model for container throughput forecasting at ports are proposed. The proposed hybrid approaches are compared empirically with each other and with other benchmark methods in terms of measurement criteria on the forecasting performance. The results suggest that the proposed hybrid approaches can achieve better forecasting performance than individual approaches. It is implied that the description of the seasonal nature and nonlinear characteristics of container throughput series is important for good forecasting performance, which can be realized efficiently by decomposition and the “divide and conquer” principle. [Copyright &y& Elsevier]

For time series, the problem that we often encounter is how to extract the patterns hidden in the real world data for forecasting its future values. A single linear or nonlinear model is inadequate in modeling and forecasting the time series, because most of them usually contain both linear and nonlinear patterns. This study constructs a hybrid forecasting model that combines autoregressive integrated moving average (ARIMA) with Elman artificial neural network (ANN) for short-term forecasting of time series. The proposed approach considers the linear and nonlinear patterns in the real data simultaneously so that it can mine more precise characteristics to describe the time series better. Finally, the forecasting results of the hybrid model are adjusted with the knowledge from text mining and expert system. The empirical results on the container throughput forecast of Tianjin Port show that the forecasts by the hybrid model are superior to those of ARIMA model and Elman network. [ABSTRACT FROM AUTHOR]

This paper proposes that sales and demands information are equally important in the supply chain. It discusses the role of factors in chorological forecasting and puts forth the supply chain collaborative forecasting methods based on factors and presents the relevant empirical studies. In the light of the historical actual sales data, factors of Spring Festival transportation, shutting down for examinations, and repairs and minor repairs are extracted and quantified in different hierarchies and domains. At the same time, they are reverted in the corporate sales forecasting. The empirical studies indicate that factors play an important role in supply chain sales forecasting. Their application can greatly improve the specific and general forecasting accuracy and represents the thought of collaborative forecasting. They can contribute to the supply chain implication and prominent information application; they can contribute to positively employing the potential negative constraints of supply chain enterprises; and they can also contribute to the management of supply chain as the information whole. All these can be considered as an extension of the economic information filter in different hierarchies and modules. [ABSTRACT FROM AUTHOR]

An effective foreign exchange (forex) trading decision is usually dependent on effective forex forecasting. In this study, an intelligent system framework integrating forex forecasting and trading decision is first proposed. Based on this framework, an advanced intelligent decision support system (DSS) incorporating a back-propagation neural network (BPNN)-based forex forecasting subsystem and Web-based forex trading decision support subsystem is developed, which has been used to predict the directional change of daily forex rates and provide intelligent online decision support for financial institutions and individual investors. This article describes the forex forecasting and trading decision method, the system architecture, main functions, and operation of the developed DSS system. A comparative study is conducted between our developed system and others commonly used in order to assess the overall performance of the developed system. The assessment results show that our developed DSS outperforms some commonly used forex forecasting and trading decision systems and can provide intelligent e-service for forex traders to make useful trading decisions in the forex market. [ABSTRACT FROM AUTHOR]

Forecasting exchange rates is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. Artificial neural networks (ANNs) have been widely used as a promising alternative approach for a forecasting task because of several distinguished features. Research efforts on ANNs for forecasting exchange rates are considerable. In this paper, we attempt to provide a survey of research in this area. Several design factors significantly impact the accuracy of neural network forecasts. These factors include the selection of input variables, preparing data, and network architecture. There is no consensus about the factors. In different cases, various decisions have their own effectiveness. We also describe the integration of ANNs with other methods and report the comparison between performances of ANNs and those of other forecasting methods, and finding mixed results. Finally, the future research directions in this area are discussed. [ABSTRACT FROM AUTHOR]

This study proposes a new ensemble deep learning approach called LSTM-B by integrating long-short term memory (LSTM) neural network and bagging ensemble learning strategy in order to obtain accurate results of exchange rates forecasting and to improve profitability of exchange rates trading. Previous research literatures have explored exchange rate forecasts, mainly focusing on the validity of forecasts, nevertheless; the precision is only one aspect of exchange rates forecasts. More important than the forecasting performance is how these ensemble learning approaches such as our proposed LSTM-B ensemble deep learning approach can advise professional trading. We extend our forecasts results to examine potential financial profitability of exchange rates between the US dollars (USD) against other four major currencies, such as GBP, JPY, EUR and CNY. The empirical study indicates the effectiveness of our proposed LSTM-B ensemble deep learning approach, which significantly improved forecasting accuracy and potential trading profitability. The proposed LSTM-B ensemble deep learning approach significantly outperforms some other benchmarks with/without bagging ensemble learning strategy under study by means of the forecast performance and the potential trading profitability. [ABSTRACT FROM AUTHOR]

In this study, a novel decomposition-ensemble methodology is proposed for container throughput forecasting. Firstly, the sample data of container throughput at ports are decomposed into several components. Secondly, the time series of the various components are thoroughly investigated to accurately capture the data characteristics. Then, an individual forecasting model is selected for each component based on the data characteristic analysis (DCA). Finally, the forecasting results are combined as an aggregated output. An empirical analysis is implemented for illustration and verification purposes. Our results suggest that proposed hybrid models can achieve better performance than other methods. [ABSTRACT FROM AUTHOR]

Nowadays information technology advances allow the collecting and storage of large complex datasets in many areas. Modeling and forecasting interval-valued time series (ITS) has drawn much attention over the last two decades because interval-valued observations contain more information than point-valued observations over the same period and remove undesirable noises in high-frequency data. However, most work mainly focuses on modeling a linear univariate ITS or bivariate point process. This paper proposes nonparametric regression models for interval-valued time series with imposing constraints, e.g., monotonicity. This setting with a monotonic constraint is consistent with the existing literature, which focuses on incorporating valuable empirical information in modeling and forecasts. Two constraint estimators are developed and asymptotic properties are established. Monte Carlo simulation is conducted to show the finite sample performance. An empirical application to equity premium documents that the proposed model yields a better forecast performance than some popular models in the literature. • First applies the parsimonious nonparametric spirit to interval time series. • Provide an algorithm obtaining the constraint interval estimators with bagging. • Derive the consistency and limit distribution of the proposed estimators. • Outperforms existing methods in the interval predictive model for stock returns. [ABSTRACT FROM AUTHOR]

Abstract: In this study, a multistage nonlinear radial basis function (RBF) neural network ensemble forecasting model is proposed for foreign exchanger rates prediction. In the process of ensemble modeling, the first stage produces a great number of single RBF neural network models. In the second stage, a conditional generalized variance (CGV) minimization method is used to choose the appropriate ensemble members. In the final stage, another RBF network is used for neural network ensemble for prediction purpose. For testing purposes, we compare the new ensemble model''s performance with some existing neural network ensemble approaches in terms of four exchange rates series. Experimental results reveal that the predictions using the proposed approach are consistently better than those obtained using the other methods presented in this study in terms of the same measurements. [Copyright &y& Elsevier]

Crude oil price forecasting has garnered considerable attention due to its pivotal role in both market dynamics and economic stability. In this study, we present an attention-based principal component analysis (attention-PCA) methodology designed to improve the performance of oil price forecasting models. The attention-PCA approach enables greater focus on predictor variables with superior forecasting capabilities. Furthermore, we develop a diversity enhancement mechanism for forecast combination by incorporating multiple attention mechanisms, varying numbers of principal components, and a range of forecasting models. The empirical results demonstrates that attention-PCA-based individual forecasting models significantly outperform benchmark models, reducing the Mean Absolute Percentage Error (MAPE) by up to 43.2%. The proposed forecast combination strategy yields the most accurate and diverse forecasts among those evaluated, with the MAPE of the optimal combination model standing at 4.40%. • A semi-heterogeneous forecast combination approach to crude oil price is proposed. • Attention-PCA assigns more attention to factors with superior forecast capacity. • A diversity enhancement mechanism for forecast combination is presented. • The proposed forecast combination method yields both accurate and diverse forecasts. [ABSTRACT FROM AUTHOR]

To enhance the accuracy of interval carbon price forecasting, this study proposes a secondary decomposition-ensemble framework. Firstly, the bivariate empirical mode decomposition (BEMD) is applied for primary decomposition of the original interval-valued time series (ITS). Next, the multi-scale permutation entropy (MPE) is introduced to measure the unpredictability of each decomposed component ITS, and the multivariate variational mode decomposition (MVMD) is employed to implement secondary decomposition of the component ITS with the highest complexity. Then, a sparrow search algorithm-enhanced interval multi-layer perceptron (SSA-iMLP) is developed for forecasting each component ITS. Finally, all forecasts of component ITSs are aggregated into ITS forecasts of carbon prices. Using carbon price ITS data from Hubei and Guangdong Emission Exchanges in China, empirical analysis is conducted. The results show that our proposed model has higher predictive accuracy and stronger robustness than benchmark models, indicating that the framework is promising for ITS forecasting in complex scenarios. • Bivariate empirical mode decomposition is used for primary decomposition. • Multi-scale permutation entropy is introduced to measure the unpredictability. • A secondary decomposition is performed to the component ITS with the highest complexity. • An interval multi-layer perception model with sparrow search algorithm is developed. • Our proposed model exhibits superiority of predictive accuracy. [ABSTRACT FROM AUTHOR]

Structural changes often occur in air passengers due to some external factors such as airport expansion, policy orientation and economic development; model uncertainty is a common long-standing issue in forecasting. To address these issues, a novel time-varying Jackknife model averaging method (TVJMA) (Sun et al, 2020, 2012) is employed to predict air passengers of the Top 5 airports in China. Based on nonparametric estimation, the optimal time-varying weights for various candidate models with time-varying parameters in candidate models are obtained by minimizing the local Jackknife criterion at every time point t. TVJMA method allows the weights and parameters to change over time. Empirical results show that the TVJMA method used in this paper is significantly superior to other benchmark models, including Hansen and Racine's (2012) Jackknife model averaging method (JMA), autoregression model (AR), autoregression integrated moving average model (ARIMA), seasonal autoregression integrated moving average model (SARIMA), and time-varying parameter model (TVP). Furthermore, the predictive effect of TVJMA is robust to different test sets and prediction steps. Overall, TVJMA method effectively reduces the predictive risk caused by structural changes and model uncertainty, and thus produces accurate and stable forecasts of air passengers. [ABSTRACT FROM AUTHOR]

Crude oil plays an important role in economic development and political stability, and many scholars have been committed to forecasting its price. However, its influencing factors are complex and diverse, and previous studies have rarely focused on the second multivariate decomposition. Therefore, this study introduces financial market factors and crude oil news as forecasters, and proposes a novel hybrid model with two-layer multivariate decomposition. To verify the performance of the proposed model, an empirical study is performed on weekly West Texas Intermediate (WTI) oil spot price. The results suggest that the second multivariate decomposition for the high-frequency subcomponent can significantly improve the forecasting accuracy, and the forecasting performance of the proposed model outperforms all the benchmark models. • A novel model with two-layer multivariate decomposition and news text is proposed. • Crude oil news variables are constructed based on keywords. • A new means to determine lag order is constructed with statistics and chaos theory. [ABSTRACT FROM AUTHOR]

Current research highlights the efficacy of decomposition and ensemble algorithms in enhancing forecasting accuracy; however, the investigation of robustness associated with decomposed components within these algorithms remains notably scarce in existing literature. To address this gap, we introduce a novel tourism demand forecasting framework, underpinned by a sophisticated decomposition algorithm. Our approach initially decomposes the original data into multiple sub-series and subsequently selects forecasting models based on their respective data attributes. We evaluated the proposed framework by forecasting monthly tourist arrivals in Hong Kong from six countries. The superiority of the novel decomposition method was further substantiated through comparisons with alternative decomposition techniques in both single-step and multi-step ahead forecasting contexts. The results indicate that the proposed forecasting framework consistently outperforms baseline models in terms of forecasting accuracy across all tourism demand forecasting scenarios, with the innovative decomposition method exhibiting exceptional performance. [ABSTRACT FROM AUTHOR]

Accurate ultra-short-term wind power forecasting is a prerequisite for decision making related to the management of power systems. Existing approaches used to forecast wind power ignored the correlation between wind power outputs under similar wind power, which provides important information for wind power forecasting. A spatiotemporal approach based on an indirect strategy and a multi-factor extraction model is proposed in this study to achieve more accurate power prediction. Specifically, wind power and other variables are classified into four categories according to wind direction, and multivariate feature extraction and wind power forecasting are performed separately for each category. In the experimental study, taking two real-world datasets of wind farms in Northeast China as examples, the proposed approach is compared with eight benchmarking approaches, and the results demonstrate the effectiveness and robustness of our approach. In addition, we further conduct the DM test, and the sensitivity analysis to discuss the effect of hyperparameters on forecasting performance. The results of DM verify the superiority of the proposed approach in statistic. This study provides a new high-precision approach and a new forecasting strategy for future wind power forecasting. • An indirect strategy is adopted in the wind power forecasting. • The dependence between data under similar wind direction is considered. • The approach characterizes the spatiotemporal effects of wind power. • This paper uses various information extraction and filtering methods. • Comprehensive experimental studies are conducted. [ABSTRACT FROM AUTHOR]

Short-term load forecasting has evolved into an important aspect of power system in safe operation and rational dispatching. However, given the load series' instability and volatility, this is a challenging task. To this end, this study proposes a dynamic decomposition-reconstruction-ensemble approach by cleverly and dynamically combining two proven and effective techniques (i.e., the reconstruction techniques and the secondary decomposition techniques). In fact, by introducing the decomposition-reconstruction process based on the dynamic classification, filtering, and giving the criteria for determining the components that need to be decomposed again, our proposed model improves the decomposition-ensemble forecasting framework. Our proposed model makes full use of decomposition techniques, complexity analysis, reconstruction techniques, secondary decomposition techniques, and a neural network optimized by an automatic hyperparameter optimization algorithm. Besides, we compared our proposed model with state-of-the-art models including five models with reconstruction techniques and two models with secondary decomposition techniques. The experiment results demonstrate the superiority of our proposed dynamic decomposition-reconstruction technique in terms of forecasting accuracy, precise direction, equality, stability, correlation, comprehensive accuracy, and statistical tests. To conclude, our proposed model has the potential to be a useful tool for short-term load forecasting. • A dynamic decomposition-reconstruction strategy is proposed for time series forecasting. • Improve the existing decomposition-ensemble forecasting framework. • Three sets of real load data were utilized to evaluate the forecasting performance. • Experiments verify the effectiveness of dynamic decomposition-reconstruction strategy. [ABSTRACT FROM AUTHOR]

• Strategies of previous tourism literature are summarized. • A meticulous review and comparison of five strategies is conducted. • The performance of five forecasting strategies is explored and compared. • The DIRMO strategy is most practical among all these strategies. • Empirical findings provide a reference paradigm for future research. Previous researches have proposed five strategies to deal with complex multi-step ahead forecasting tasks. However, these strategies have not received much attention in the field of tourism research and the performance of them is still unknown. Accordingly, we summarize the strategies used in multi-step ahead tourism demand forecasting articles and produce a comparative analysis of five strategies. By employing nine tourist arrival time series in Hong Kong, the study explores the performance of different strategy-driven forecasting approaches in tourism demand. The empirical results show that Direct and DIRMO (s = 2) strategies perform better than other strategies in terms of forecasting accuracy, but there is no significant difference between them, excepting the latter is lower than the former in computational complexity. [ABSTRACT FROM AUTHOR]

Accurate electricity consumption forecasting plays a significant role in power production and supply and power dispatching. Thus, a new hybrid model combing a grey model with fractional order accumulation, called FGM (1, 1), with seasonal factors, sine cosine algorithm (SCA), and an error correction strategy is proposed in this research. To accurately predict the seasonal fluctuations, seasonal factors are used in this model; Then, with the aim of improving the prediction performance, a SFGM (1, 1) model optimized by SCA rather than least square method, namely SCA-SFGM (1, 1), is establish to forecast electricity consumption; Moreover, considering forecasting error sequence may contain useful information, an error correction strategy is introduced to model forecasting error time series to adjust the preliminary forecasts of SCA-SFGM (1, 1). Fourth, four comparison models, three measurement criteria and a statistical hypothesis testing method using monthly residential electricity consumption dataset from 2015 to 2020 are designed to verify the prediction performance of models; Lastly, experimental results show that the mean absolute percentage error (MAPE) of the proposed model is 4.1698%, which is much lower than 14.5642%, 6.5108%, 5.9472%, 5.7060% and 4.9219% of GM (1, 1), SARIMA, SGM (1, 1), SFGM (1, 1) and SCA-SFGM (1, 1) models, respectively, showing that the proposed model can not only effectively capture seasonal fluctuations, it also adds an operational candidate forecasting benchmark model in electricity markets. [Display omitted] • Proposed a model using an optimized SFGM and an error correction strategy. • The proposed model can reveals the changing trends and seasonal fluctuations. • Three criteria, five benchmark models and a hypothesis testing are constructed. • The proposed model is proved to have higher accuracy than comparison models. [ABSTRACT FROM AUTHOR]

Despite the considerable deployed resources, current policing efforts are failing to stop crimes before they start, and therefore, also failing to adequately protect lives and property. To promote the intelligent transformation from reactive to proactive policing, this study proposed a hierarchical crime prediction framework. First, the temporal dependency in the frequency domain was decomposed and a network constructed to capture the spatial relationships within the sub-frequencies. Human mobility in a city was then utilized to characterize the dynamic relationships within the network. Using the proposed framework, this study examined the crime distribution evolution in Chicago to holistically predict the short-term crimes in the different communities. The framework was found to have high predictive accuracy and significant potential in promoting proactive policing. It was concluded that: (1) as the crime distribution evolution comes from the spatial relationship changes, these dynamic relationships are critical in explaining and characterizing the evolution; and (2) the social interactions constructed using the human activity data can characterize the dynamic crime distribution relationships. • Development of a model to forecast short-term crime in a community network. • Decomposing time series and capturing spatial patterns within decompositions. • Utilizing taxis to proxy mobility within and between communities. • Explaining how crime distribution change within and across communities. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) techniques have substantially changed the research paradigm in the field of air quality forecasting due to their powerful performance. Considering the improvement in the availability of air quality data and the rapid proliferation of AI techniques, it is necessary to comprehensively and quantitatively review the development of air quality forecasting with AI techniques during the last two decades (2000–2019) by scientometric and content analysis. First, an overview of the relevant countries, institutions, authors, journals, and papers is presented. Then, the research hotspots and frontier evolution are explored by adopting reference co-citation analysis and keyword co-occurrence analysis. Furthermore, this study conducts a content analysis to investigate current topical interests to identify research gaps and propose future research directions. The analytical framework and the findings provide helpful insights into the prospects in air quality forecasting with AI techniques. • Air quality forecasting with AI is investigated deeply and comprehensively. • A top-down review integrating scientometric and content analysis is adopted. • Various statistics and visualizations of this research field are provided. • The research hotspots and frontier evolution in this research field are explored. [ABSTRACT FROM AUTHOR]

• A decomposition–ensemble model is proposed for interval-valued load forecasting. • Multivariate multiscale permutation entropy technique is utilized to perform complexity analysis on decomposed interval-valued components for capturing internal features and reconstructing the components. • An automatic Bayesian optimization algorithm based on the Tree-structured Parzen Estimator algorithm is used in hyperparameter optimization. • Empirical results verify that the proposed approach outperforms other benchmarks under study. Short-term load forecasting is crucial for power demand-side management and the planning of the power system. Considering the necessity of interval-valued time series modeling and forecasting for the power system, this study proposes an interval decomposition-reconstruction-ensemble learning approach to forecast interval-valued load, in terms of the concept of "divide and conquer". First, bivariate empirical mode decomposition is applied to decompose the original interval-valued data into a finite number of bivariate modal components for extracting and identifying the fluctuation characteristics of data. Second, based on the complexity analysis of each bivariate modal component by multivariate multiscale permutation entropy, the components were reconstructed for capturing inner factors and reduce the accumulation of estimation errors. Third, long short-term memory is utilized to synchronously forecast the upper and the lower bounds of each bivariate component and optimized by the Bayesian optimization algorithm. Finally, generating the aggregated interval-valued output by ensemble the forecasting results of the upper and lower bounds of each component severally. The electric load of five states in Australia is used for verification, and the empirical results show that the forecasting accuracy of our proposed learning approach is significantly superior to single models and the decomposition-ensemble models without reconstruction. This indicates that our proposed learning approach appears to be a promising alternative for interval load forecasting. [ABSTRACT FROM AUTHOR]

Current electricity price forecasting models rely on only simple hybridizations of data preprocessing and optimization methods while ignoring the significance of adaptive data preprocessing and effective optimization and selection strategies to obtain optimal models that improve the forecasting performance. To solve these problems, this study develops an improved electricity price forecasting model that offers the advantages of adaptive data preprocessing, advanced optimization method, kernel-based model, and optimal model selection strategy. Specifically, the adaptive parameter-based variational mode decomposition technology is proposed to provide desirable data preprocessing results, and a leave-one-out optimization strategy based on the chaotic sine cosine algorithm is proposed and applied to develop optimal kernel-based extreme learning machine models. In addition, a newly proposed optimal model selection strategy is applied to determine the developed model that provides the most desirable forecasting result. Numerical results show that the developed model's performance metrics were best, and the average values of mean absolute error, root mean square error, mean absolute percentage error, index of agreement, and Theil's inequality coefficient in four datasets are 0.5121, 0.7607, 0.5722%, 0.9997 and 0.0041, respectively, which imply that the developed model is a promising, applicable and effective electricity price forecasting technique in the real electricity market. • Optimal model selection strategy is proposed for improving forecasting performance. • Leave-one-out optimization strategy is proposed for developing an optimal model. • The significance of adaptive data preprocessing is considered in this study. • The developed model exhibits better performance for modeling electricity price. [ABSTRACT FROM AUTHOR]