Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index
پیش بینی حجم گردشگر موثر پشتیبانی شده توسط PCA و BPNN بهبود یافته با استفاده از شاخص بایدو-2018
The precise forecasting of tourist volume is a very challenging task. This paper aims to propose an effective model named PCA-ADE-BPNN for forecasting tourist volume based on Baidu index. The principal component analysis (PCA), a dimensional reduction, is employed to decorrelate the input data before training a back propagation neural network (BPNN) architecture, and the adaptive differential evolution algorithm (ADE) is for getting global optimization of BP networks weight values and threshold values to enhance the forecasting performance of BPNN. The PCA-ADE-BPNN model is a new combination of a dimensional reduction algorithm, an optimization algorithm, and a neural network. The validity of this model is demonstrated by conducting case studies of Beijing City and Hainan Province, China. The results indicate the proposed PCA-ADE-BPNN always outperforms other models in terms of forecasting accuracies. Therefore, the proposed PCA-ADE-BPNN is a potential candidate for the effective forecasting of tourist volume.
keywords: Tourist volume forecasting |Principal component analysis |Baidu index |Back-propagation neural network |Adaptive differential evolution
Agribusiness time series forecasting using Wavelet neural networks and metaheuristic optimization: An analysis of the soybean sack price and perishable products demand
پیش بینی سری های زمانی کسب و کارهای کشاورزی با استفاده از شبکه های عصبی موج کوچک و بهینه سازی اکتشافی ذهنی متا: یک تحلیل روی قیمت یک گونی سویبان و تقاضای محصولات فاسد شدنی-2018
Brazilian agribusiness is responsible for almost 25% of the country gross domestic product, and companies from this economic sector may have strategies to control their actions in a competitive market. In this way, models to properly predict variations in the price of products and services could be one of the keys to the success in agribusiness. Consistent models are being adopted by companies as part of a decision making process when important choices are based on short or long-term forecasting. This work aims to evaluate Wavelet Neural Networks (WNNs) performance combined with five optimization techniques in order to obtain the best time series forecasting by considering two case studies in the agribusiness sector. The first one adopts the soybean sack price and the second deals with the demand problem of a distinct groups of products from a food company, where nonlinear trends are the main characteristic on both time series. The optimization techniques adopted in this work are: Differential Evolution, Artificial Bee Colony, Glowworm Swarm Optimization, Gravitational Search Algorithm, and Imperialist Competitive Algorithm. Those were evaluated by considering short-term and long-term forecasting, and a prediction horizon of 30 days ahead was considered for the soybean sack price case, while 12 months ahead was selected for the products demand case. The performance of the optimization techniques in training the WNN were compared to the well-established Backpropagation algorithm and Extreme Learning Machine (ELM) assuming accuracy measures. In long-term forecasting, which is considered more difficult than the short-term case due to the error accumulation, the best combinations in terms of precision was reached by distinct methods according to each case, showing the importance of testing different training strategies. This work also showed that the prediction horizon significantly affected the performance of each optimization method in different ways, and the potential of assuming optimization in WNN learning process.
keywords: Agribusiness |Artificial neural networks |Time series forecasting |Metaheuristics |Natural computing |Optimization
Operating an environmentally sustainable city using fine dust level big data measured at individual elementary schools
مدیریت یک شهر سازگار با محیط زیست با استفاده از داده های بزرگ گرد و غبار، اندازه گیری شده در مدارس ابتدایی فردی-2018
As the problem of fine dust pollution becomes increasingly serious in South Korea, the country is becoming more interested in obtaining information on fine dust levels. Fine dust level data are sufficiently local to make regional forecasting meaningless. Thus, this study proposes an alternative measurement technique to minimize differ ences between published and perceived levels of fine dusts. Owing to the large variations in the fine dust levels within urban areas, it is very difficult to provide measurements that are sufficiently area-representative. Because infants and elementary school students are more sensitive to fine dust than adults, it is useful to construct large data sets of measurements of fine dust levels at elementary schools. In Korea, the distribution of elementary schools is consistent with population density, which is useful for analyzing local differences in the fine dust levels in urban areas. This study will provide a basis for big data application to public health policy and infographics using color fuzzy model.
Keywords: Fine dust ، Big data ، Sustainable city ، Public health policy ، Infographics ، Color fuzzy model
Stay alert: Forecasting the risks of sexting in Korea using social big data
هشدار: پیش بینی خطرات جنسیت در کره با استفاده از داده های بزرگ اجتماعی-2018
Youth sexting, which is commonly defined as the intimate image sharing of persons under 18, is an emerging phenomenon that has garnered significant attention in South Korea and in particular, the South Korean government. Widely recognized for its potential to generate undue harm, the South Korean government has initiated a movement determined to block the participation of obscene content sharing between youths under the age of 18. While there may be different avenues to examine this phenomenon from, an approach notably absent from this list is the use of big data and data mining information produced via the dispersion of the Internet and social media. Using social big data, the study found that teenagers sexting in hopes of obtaining a higher volume of attention among friends; file sharing is more frequented than image distribution through sexting; and transactions without “adult pornography” and with “smishing” were the most influential in addressing the risks of sexting in South Korea. While big data and data mining do not make any inferences themselves, the benefits of analyzing social big data lies in its ability to incorporate a much larger volume of data and confirm the thoughts of a diverse range of participants.
Keywords: Social big data ، Data mining ، Youth sexting ، South Korea ، Trends and patterns
Scenario-based planning for tourism development using system dynamic modelling: A case study of Cat Ba Island, Vietnam
برنامه ریزی مبتنی بر طرح برای توسعه گردشگری با استفاده از مدلسازی پویای سیستم: یک مطالعه موردی روی جزیره کت با، ویتنام-2018
Tourism destinations are dynamically complex systems in which behaviour is controlled by many interacting components and feedback loops. Yet tourism destination planning has traditionally been based on forecasting models that rely on historical data to predict future trends. We explore system dynamic modelling as an alternative to forecasting models for the scenario-based planning of tourism destinations. We construct a system dynamic model for tourism development on Cat Ba Island, a rapidly developing tourist destination in Vietnam, and use it to model alternative tourism development scenarios. Our results indicate that the current trajectory of tourism development on Cat Ba Island is not sustainable and limits to growth may be reached as early as 2022 due to water shortages, pollution and overcrowding. Beyond this time the destination risks breaching its limits to growth, which creates a further risk, that of eroding carrying capacity through resource depletion and environmental degradation.
keywords: Scenario planning |System dynamic modelling |Sustainable tourism |Decision making
A dynamic neural network architecture with immunology inspired optimization for weather data forecasting
یک معماری شبکه عصبی پویا با ایمنولوژی بهینه سازی برای پیش بینی داده های آب و هوایی-2018
Recurrent neural networks are dynamical systems that provide for memory capabilities to recall past behaviour, which is necessary in the prediction of time series. In this paper, a novel neural network architecture inspired by the immune algorithm is presented and used in the forecasting of naturally occurring signals, including weather big data signals. Big Data Analysis is a major research frontier, which attracts extensive attention from academia, industry and government, particularly in the context of handling issues related to complex dynamics due to changing weather conditions. Recently, extensive deployment of IoT, sensors, and ambient intelligence systems led to an exponential growth of data in the climate domain. In this study, we concentrate on the analysis of big weather data by using the Dynamic Self Organized Neural Network Inspired by the Immune Algorithm. The learning strategy of the network focuses on the local properties of the signal using a self-organised hidden layer inspired by the immune algorithm, while the recurrent links of the network aim at recalling previously observed signal patterns. The proposed network exhibits improved performance when compared to the feedforward multilayer neural network and state-of-the-art recurrent networks, e.g., the Elman and the Jordan networks. Three non-linear and non-stationary weather signals are used in our experiments. Firstly, the signals are transformed into stationary, followed by 5-steps ahead prediction. Improvements in the prediction results are observed with respect to the mean value of the error (RMS) and the signal to noise ratio (SNR), however to the expense of additional computational complexity, due to presence of recurrent links.
Keywords: Recurrent Neural Networks ،Immune Systems Optimisation، Time Series Data analytics ، weather forecasting
Financial market Volatility, macroeconomic fundamentals and investor Sentiment
فراریت بازار مالی، اساس های اقتصاد کلان و احساسات سرمایه گذار-2018
In this paper, we investigate the dynamic relationship between financial market volatility, macroeconomic fundamentals and investor sentiment, employing a two-factor model to decompose volatility into a persistent long run component and a transitory short run component. Using a structural VAR model with Bayesian sign restrictions, we show that adverse shocks to aggregate demand and supply cause an increase in the persistent component of both stock and bond market volatility, and that adverse shocks to the persistent component of either stock or bond market volatility cause a deterioration in macroeconomic fundamentals. We find no evidence of a relationship between the transitory component of volatility and macroeconomic fundamentals. Instead, we find that the transitory component is more closely associated with changes in investor sentiment. Our results are robust to a wide range of alternative specifications. Out-of-sample forecasting shows that the components of volatility can improve forecasts of macroeconomic fundamentals, and vice versa.
keywords: Stock and bond market volatility |Two-factor volatility model |Macroeconomic fundamentals |Structural vector autoregression |Bayesian estimation
Modeling and long-term forecasting demand in spare parts logistics businesses
مدلسازی و پیش بینی بلند - مدت تقاضا در کسب و کارهای لوازم بخشهای یدکی-2018
In order to provide high service levels, companies competing in the electronics manufacturing sector need to ensure the availability of spare parts for repair and maintenance operations. This paper examines the purchase life-cycles of electronic spare parts and presents a new way of modeling and forecasting spare part demand for electronic commodities in the spare parts logistics services. The presented modeling methodology is founded on the assumption that the purchase life-cycles of spare parts can be described by a curve with short term fluctuations around it. For this purpose, a flexible Demand Model Function is introduced. The proposed forecasting method uses a knowledge discovery-based approach that is built upon the combined application of analytic and soft computational techniques and is able to indicate the turning points of the purchase life-cycle curve. The novelty lies in the fact that the model function has certain characteristics which support describing and interpreting the demand trend as a function of time. The application of our methodology is mainly advantageous in long-term forecasting, it can be especially useful in supporting purchase planning decisions in the ramp-up and declining phases of purchase life-cycles of product specific spare parts. A demonstrative example is used to illustrate the applicability of the proposed methodology. Its forecasting capability is compared to those of some widely applied methods in business practice. From the results, the new method may be viewed as a viable alternative spare part demand forecasting technique in spare part logistics sector.
keywords: Spare part logistics |Electronic aftermarket services |Purchase life-cycle forecasting |Knowledge discovery |Clustering time series
Compression of smart meter big data_ A survey
فشرده سازی داده های بزرگ متریک هوشمند : یک مرور-2018
In recent years, the smart grid has attracted wide attention from around the world. Large scale data are collected by sensors and measurement devices in a smart grid. Smart meters can record fine-grained information about electricity consumption in near real-time, thus forming the smart meter big data. Smart meter big data has provided new opportunities for electric load forecasting, anomaly detection, and demand side management. However, the high-dimensional and massive smart meter big data not only creates great pressure on data transmission lines, but also incur enormous storage costs on data centres. Therefore, to reduce the transmission pressure and storage overhead, improve data mining efficiency, and thus fulfil the potential of smart meter big data. This study presents a comprehensive study on the compression techniques for smart meter big data. The development of smart grids and the characteristics and application challenges of electric power big data are first introduced, followed by analysis of the characteristics and benefits of smart meter big data. Finally, this study focuses on the potential data compression methods for smart meter big data, and discusses the evaluation methods for smart meter big data compression.
Keywords: Smart grid ، Smart meter ، Energy big data ، Data compression
فشرده سازی هوشمند برای داده های بزرگ: مرور
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 40
در سال های اخیر، شبکه هوشمند توجه گسترده ای از سراسر جهان را به خود جلب کرده است. داده های مقیاس بزرگ توسط سنسور ها و دستگاه های اندازه گیری در یک شبکه هوشمند جمع آوری می شوند. مقیاس هوشمند می تواند اطلاعات دقیق در مورد مصرف الکتریسیته را در زمان واقعی به ثبت برساند، بنابراین داده های بزرگ در مقیاس هوشمند اندازه گیری می شود. داده های بزرگ مقیاس هوشمند فرصت های جدیدی برای پیش بینی بار الکتریکی، کشف عادت ها و مدیریت تقاضا ارائه داده است. با این حال، ابعاد بزرگ و داده های بزرگ در مقیاس هوشمند عظیم نه تنها فشار زیادی را بر خطوط انتقال داده ایجاد می کند، بلکه هزینه های ذخیره سازی زیادی را در مراکز داده نیز به همراه می آورد. بنابراین، برای کاهش فشار انتقال و ارتفاع محل ذخیره سازی، برای بهبود راندمان استخراج داده ها، و به اين ترتيب ظرفیت های تحقق هوشمند داده های بزرگ 130 سانتی متری است. مقاله پیش رو یک مطالعه جامع در مورد تکنیک های فشرده سازی داده های بزرگ هوشمند را ارائه می دهد. توسعه شبکه های هوشمند و خصوصیات و چالش های کاربرد داده های بزرگ الکتریکی ابتدا معرفی شده و سپس تجزیه و تحلیل ویژگی ها و مزایای داده های بزرگ مقیاس بزرگ انجام می پذیرد. در نهایت، این مطالعه بر روی روش های فشرده سازی اطلاعات بالقوه برای داده های بزرگ هوشمند تمرکز می کند و روش های ارزیابی فشرده سازی داده های مقیاس هوشمند را مورد بحث قرار می دهد.
کلمات کلیدی: شبکه هوشمند | مقیاس هوشمند | داده های بزرگ انرژی | فشرده سازی داده ها.
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