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João Sequeira, Jorge Louçã, António M. Mendes and Pedro G. Lind
We analyze the empirical series of malaria incidence, using the concepts of autocorrelation, Hurst exponent and Shannon entropy with the aim of uncovering hidden variables in those series. From the simulations of an agent model for malaria spreading, we ...
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Shelton Peiris, Manabu Asai and Michael McAleer
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Yuan Chen and Abdul Q. M. Khaliq
The Lee?Carter model could be considered as one of the most important mortality prediction models among stochastic models in the field of mortality. With the recent developments of machine learning and deep learning, many studies have applied deep learni...
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Halit Apaydin, Hajar Feizi, Mohammad Taghi Sattari, Muslume Sevba Colak, Shahaboddin Shamshirband and Kwok-Wing Chau
Due to the stochastic nature and complexity of flow, as well as the existence of hydrological uncertainties, predicting streamflow in dam reservoirs, especially in semi-arid and arid areas, is essential for the optimal and timely use of surface water res...
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Yumin Su, Jianfeng Lin, Dagang Zhao, Chunyu Guo, Chao Wang and Hang Guo
In marine environments, ships are bound to be disturbed by several external factors, which can cause stochastic fluctuations and strong nonlinearity in the ship motion. Predicting ship motion is pivotal to ensuring ship safety and providing early warning...
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Guglielmo Maria Caporale, Luis Gil-Alana and Tommaso Trani
This paper applies long-memory techniques (both parametric and semi-parametric) to examine whether Brexit has led to any significant changes in the degree of persistence of the FTSE (Financial Times Stock Index) 100 Implied Volatility Index (IVI) and of ...
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C. Tamilselvi, Md Yeasin, Ranjit Kumar Paul and Amrit Kumar Paul
Denoising is an integral part of the data pre-processing pipeline that often works in conjunction with model development for enhancing the quality of data, improving model accuracy, preventing overfitting, and contributing to the overall robustness of pr...
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Lan Wang, Mingjiang Xie, Min Pan, Feng He, Bing Yang, Zhigang Gong, Xuke Wu, Mingsheng Shang and Kun Shan
Harmful algal blooms (HABs) have been deteriorating global water bodies, and the accurate prediction of algal dynamics using the modelling method is a challenging research area. High-frequency monitoring and deep learning technology have opened up new ho...
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Sophia-Ekaterini Avgerinou, Eleni-Apostolia Anyfadi, Georgios Michas and Filippos Vallianatos
Greece is one of Europe?s most seismically active areas. Seismic activity in Greece has been characterized by a series of strong earthquakes with magnitudes up to Mw = 7.0 over the last five years. In this article we focus on these strong events, namely ...
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Ayushi Chahal, Preeti Gulia, Nasib Singh Gill and Ishaani Priyadarshini
IoT devices collect time-series traffic data, which is stochastic and complex in nature. Traffic flow prediction is a thorny task using this kind of data. A smart traffic congestion prediction system is a need of sustainable and economical smart cities. ...
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Kejing Zhao, Jinliang Zhang and Qing Liu
The reasonable pricing of options can effectively help investors avoid risks and obtain benefits, which plays a very important role in the stability of the financial market. The traditional single option pricing model often fails to meet the ideal expect...
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Tahani Alqurashi
Arabic dialect identification (ADI) has recently drawn considerable interest among researchers in language recognition and natural language processing fields. This study investigated the use of a character-level model that is effectively unrestricted in ...
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Azharul Islam and KyungHi Chang
Unstructured data from the internet constitute large sources of information, which need to be formatted in a user-friendly way. This research develops a model that classifies unstructured data from data mining into labeled data, and builds an information...
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Yong Han, Cheng Wang, Yibin Ren, Shukang Wang, Huangcheng Zheng and Ge Chen
The accurate prediction of bus passenger flow is the key to public transport management and the smart city. A long short-term memory network, a deep learning method for modeling sequences, is an efficient way to capture the time dependency of passenger f...
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