Neural Network Forecasting of Tourism Demand

2005 ◽  
Vol 11 (3) ◽  
pp. 301-328 ◽  
Author(s):  
Sen Cheong Kon ◽  
Lindsay W. Turner
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Accurate Method ◽  
Future Research ◽  
Data Series ◽  
Structural Method ◽  
Short Term ◽  
Neural Network Models ◽  
The Neural Network ◽  
Short Term Forecasting

In times of tourism uncertainty, practitioners need short-term forecasting methods. This study compares the forecasting accuracy of the basic structural method (BSM) and the neural network method to find the best structure for neural network models. Data for arrivals to Singapore are used to test the analysis while the naïve and Holt-Winters methods are used for base comparison of simpler models. The results confirm that the BSM remains a highly accurate method and that correctly structured neural models can outperform BSM and the simpler methods in the short term, and can also use short data series. These findings make neural methods significant candidates for future research.

Short-term forecasting of groundwater levels under conditions of mine-tailings recharge using wavelet ensemble neural network models

2014 ◽  
Vol 23 (1) ◽  
pp. 121-141 ◽  
Author(s):  
Bahaa Khalil ◽  
Stefan Broda ◽  
Jan Adamowski ◽  
Bogdan Ozga-Zielinski ◽  
Amanda Donohoe
Keyword(s):  
Neural Network ◽  
Mine Tailings ◽  
Network Models ◽  
Short Term ◽  
Neural Network Models ◽  
Groundwater Levels ◽  
Ensemble Neural Network ◽  
Short Term Forecasting

scholarly journals FEEDFORWARD NEURAL NETWORKS AND THE FORECASTING OF MULTI-SECTIONAL DEMAND FOR TELECOM SERVICES: A COMPARATIVE STUDY OF EFFECTIVENESS FOR HOURLY DATA

2020 ◽  
Vol 19 (3) ◽  
pp. 13-25
Author(s):  
Paweł Kaczmarczyk
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Feedforward Neural Networks ◽  
Neural Model ◽  
Feedforward Neural Network ◽  
Neural Network Models ◽  
Explanatory Variables ◽  
The Neural Network ◽  
Hourly Data ◽  
Short Term Forecasting

The presented research focuses on the construction of a model to effectively forecast demand for connection services – it is thus relevant to the Prediction System (PS) of telecom operators. The article contains results of comparative studies regarding the effectiveness of neural network models and regressive-neural (integrated) models, in terms of their short-term forecasting abilities for multi-sectional demand of telecom services. The feedforward neural network was used as the neural network model. A regressive-neural model was constructed by fusing the dichotomous linear regression of multi-sectional demand and the feedforward neural network that was used to model the residuals of the regression model (i.e. the residual variability). The response variable was the hourly counted seconds of outgoing calls within the framework of the selected operator network. The calls were analysed within: type of 24 hours (e.g. weekday/weekend), connection categories, and subscriber groups. For both compared models 35 explanatory variables were specified and used in the estimation process. The results show that the regressive-neural model is characterised by higher approximation and predictive capabilities than the non-integrated neural model.

scholarly journals Devizaárfolyam előrejelzés neurális háló modell segítségével = Foreign Exchange Rate Forecast Using Neural Network

2021 ◽  
Vol 16 (1) ◽  
pp. 117-137
Author(s):  
Zsolt Lakatos
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Foreign Exchange Rate ◽  
Neural Models ◽  
Short Term ◽  
Neural Network Models ◽  
Technical Indicators ◽  
The Neural Network ◽  
Master's Thesis ◽  
Technical Indicator

Modelljeimben a technikai indikátorok használatát kapcsolom össze a neurális hálós modellek előrejelző képességeivel. A technikai indikátorok használata mellett szól, hogy rövid távon a pénzügyi idősorok autokorreláltak, a neurális modellek pedig nemlineáris kapcsolatok modellezésére alkalmasak. A kapott eredmények révén azt a következtetést vontam le, hogy ugyan a neurális háló modellek optimalizációs képessége nagyon jó és alkalmazásukkal a megfelelő technikai indikátorok is meghatározhatók, de csak lassan képesek rátanulni az adatokra, így még a legkisebb tranzakciós költség alkalmazása mellett is csak a kezdeti befektetésünk elvesztését tudjuk halogatni. My present paper is the shortened version of my master's thesis in finance presented in November 2015, in which I presented the results of the research implemented in the Training Center for Bankers. In my models I combine the use of technical indicators with predictive capabilities of neural network models. The use of a technical indicator suggests that in the short term the financial timeseries are autocorrelated, and neural models are suitable for modeling nonlinear relationships. Based on the results I concluded that although the optimization capabilities of the neural network models are very good and their application can be determined by the appropriate technical indicators, but learning from timeseries data takes too much time, so even with the smallest transaction cost we can only delay the loss of our initial investment.

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

Spatial Variability Aware Deep Neural Networks (SVANN): A General Approach

2021 ◽  
Vol 12 (6) ◽  
pp. 1-21
Author(s):  
Jayant Gupta ◽  
Carl Molnar ◽  
Yiqun Xie ◽  
Joe Knight ◽  
Shashi Shekhar
Keyword(s):  
Neural Network ◽  
Spatial Variability ◽  
Network Architecture ◽  
Network Models ◽  
Neural Network Architecture ◽  
Neural Network Models ◽  
Climatic Zones ◽  
The Neural Network ◽  
Plant Hardiness ◽  
Interpretation Model

Spatial variability is a prominent feature of various geographic phenomena such as climatic zones, USDA plant hardiness zones, and terrestrial habitat types (e.g., forest, grasslands, wetlands, and deserts). However, current deep learning methods follow a spatial-one-size-fits-all (OSFA) approach to train single deep neural network models that do not account for spatial variability. Quantification of spatial variability can be challenging due to the influence of many geophysical factors. In preliminary work, we proposed a spatial variability aware neural network (SVANN-I, formerly called SVANN ) approach where weights are a function of location but the neural network architecture is location independent. In this work, we explore a more flexible SVANN-E approach where neural network architecture varies across geographic locations. In addition, we provide a taxonomy of SVANN types and a physics inspired interpretation model. Experiments with aerial imagery based wetland mapping show that SVANN-I outperforms OSFA and SVANN-E performs the best of all.

scholarly journals Towards Enhanced Performance of Neural-Network-Based Fault Detection Using an Sequential D-Optimum Experimental Design

2018 ◽  
Vol 8 (8) ◽  
pp. 1290 ◽  
Author(s):  
Beata Mrugalska
Keyword(s):  
Neural Network ◽  
Experimental Design ◽  
Fault Detection ◽  
Network Models ◽  
Neural Model ◽  
Neural Network Models ◽  
Optimum Experimental Design ◽  
The Neural Network ◽  
Adaptive Thresholds ◽  
Linear Neural Network

Increasing expectations of industrial system reliability require development of more effective and robust fault diagnosis methods. The paper presents a framework for quality improvement on the neural model applied for fault detection purposes. In particular, the proposed approach starts with an adaptation of the modified quasi-outer-bounding algorithm towards non-linear neural network models. Subsequently, its convergence is proven using quadratic boundedness paradigm. The obtained algorithm is then equipped with the sequential D-optimum experimental design mechanism allowing gradual reduction of the neural model uncertainty. Finally, an emerging robust fault detection framework on the basis of the neural network uncertainty description as the adaptive thresholds is proposed.

scholarly journals Stock Market Prediction Using Artificial Neural Networks

2012 ◽  
Vol 6-7 ◽  
pp. 1055-1060 ◽  
Author(s):  
Yang Bing ◽  
Jian Kun Hao ◽  
Si Chang Zhang
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Learning Algorithm ◽  
Stock Exchange ◽  
Back Propagation ◽  
Composite Index ◽  
Network Models ◽  
Back Propagation Neural Network ◽  
Neural Network Models ◽  
The Neural Network

In this study we apply back propagation Neural Network models to predict the daily Shanghai Stock Exchange Composite Index. The learning algorithm and gradient search technique are constructed in the models. We evaluate the prediction models and conclude that the Shanghai Stock Exchange Composite Index is predictable in the short term. Empirical study shows that the Neural Network models is successfully applied to predict the daily highest, lowest, and closing value of the Shanghai Stock Exchange Composite Index, but it can not predict the return rate of the Shanghai Stock Exchange Composite Index in short terms.

scholarly journals Constructive Learning of Deep Neural Networks for Bigdata Analysis

2020 ◽  
Vol 9 (12) ◽  
pp. 311-322
Author(s):  
Soha Abd Mohamed El-Moamen ◽  
Marghany Hassan Mohamed ◽  
Mohammed F. Farghally
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Binary Classification ◽  
Network Models ◽  
Classification Model ◽  
Neural Network Models ◽  
Constructive Learning ◽  
The Neural Network ◽  
Rapid Pace ◽  
Better Than

The need for tracking and evaluation of patients in real-time has contributed to an increase in knowing people’s actions to enhance care facilities. Deep learning is good at both a rapid pace in collecting frameworks of big data healthcare and good predictions for detection the lung cancer early. In this paper, we proposed a constructive deep neural network with Apache Spark to classify images and levels of lung cancer. We developed a binary classification model using threshold technique classifying nodules to benign or malignant. At the proposed framework, the neural network models training, defined using the Keras API, is performed using BigDL in a distributed Spark clusters. The proposed algorithm has metrics AUC-0.9810, a misclassifying rate from which it has been shown that our suggested classifiers perform better than other classifiers.

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