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

2019 ◽  
Author(s):  
Dr. Hiral R. Patel ◽  
Satyen Parikh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Big Data ◽  
Financial Market ◽  
Data Analytics ◽  
Semantic Analysis ◽  
Big Data Analytics ◽  
Artificial Neural

scholarly journals Progress on Artificial Neural Networks for Big Data Analytics: A Survey

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 70535-70551 ◽  
Author(s):  
Haruna Chiroma ◽  
Usman Ali Abdullahi ◽  
Shafi'i Muhammad Abdulhamid ◽  
Ala Abdulsalam Alarood ◽  
Lubna A. Gabralla ◽  
...  
Keyword(s):  
Neural Networks ◽  
Big Data ◽  
Artificial Neural Networks ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Artificial Neural

Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach

2020 ◽  
pp. 004728752092124 ◽  
Author(s):  
Wolfram Höpken ◽  
Tobias Eberle ◽  
Matthias Fuchs ◽  
Maria Lexhagen
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Big Data ◽  
Web Search ◽  
Prediction Models ◽  
Arima Models ◽  
Study Results ◽  
Artificial Neural ◽  
Prediction Approach

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.

scholarly journals Deep Learning for Big Data Analytics

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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