Big Data Analytics and Artificial Neural Network Common Approach for Financial Market Prediction Utilizing Semantic Analysis

Because of high fluctuations of tourism demand, accurate predictions of tourist arrivals are of high importance for tourism organizations. The study at hand presents an approach to enhance autoregressive prediction models by including travelers’ web search traffic as external input attribute for tourist arrival prediction. The study proposes a novel method to identify relevant search terms and to aggregate them into a compound web-search index, used as additional input of an autoregressive prediction approach. As methods to predict tourism arrivals, the study compares autoregressive integrated moving average (ARIMA) models with the machine learning–based technique artificial neural network (ANN). Study results show that (1) Google Trends data, mirroring traveler’s online search behavior (i.e., big data information source), significantly increase the performance of tourist arrival prediction compared to autoregressive approaches using past arrivals alone, and (2) the machine learning technique ANN has the capacity to outperform ARIMA models.

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Big data analytics in cloud gaming: Players' patterns recognition using artificial neural networks

2016 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata.2016.7840782 ◽

2016 ◽

Cited By ~ 3

Author(s):

Victor Perazzolo Barros ◽

Pollyana Notargiacomo

Keyword(s):

Neural Networks ◽

Big Data ◽

Artificial Neural Networks ◽

Data Analytics ◽

Big Data Analytics ◽

Artificial Neural

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Neural network models in big data analytics and cyber security

2017 9th International Conference on Electronics, Computers and Artificial Intelligence (ECAI) ◽

10.1109/ecai.2017.8166441 ◽

2017 ◽

Cited By ~ 1

Author(s):

Ana-Maria Ghimes ◽

Victor-Valeriu Patriciu

Keyword(s):

Neural Network ◽

Big Data ◽

Cyber Security ◽

Data Analytics ◽

Big Data Analytics ◽

Network Models ◽

Neural Network Models

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Deep Learning for Big Data Analytics

Nature-Inspired Algorithms for Big Data Frameworks - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-5852-1.ch001 ◽

2019 ◽

pp. 1-21

Author(s):

Priti Srinivas Sajja ◽

Rajendra Akerkar

Keyword(s):

Neural Networks ◽

Big Data ◽

Deep Learning ◽

Data Analytics ◽

Big Data Analytics ◽

Future Research ◽

Cognitive Computing ◽

Intelligent Support ◽

Artificial Neural ◽

Traditional Approaches

Traditional approaches like artificial neural networks, in spite of their intelligent support such as learning from large amount of data, are not useful for big data analytics for many reasons. The chapter discusses the difficulties while analyzing big data and introduces deep learning as a solution. This chapter discusses various deep learning techniques and models for big data analytics. The chapter presents necessary fundamentals of an artificial neural network, deep learning, and big data analytics. Different deep models such as autoencoders, deep belief nets, convolutional neural networks, recurrent neural networks, reinforcement learning neural networks, multi model approach, parallelization, and cognitive computing are discussed here, with the latest research and applications. The chapter concludes with discussion on future research and application areas.

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