Big Data Analytics, Algorithms and Programming

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (15 September 2019) | Viewed by 6403

Special Issue Editors


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Guest Editor
Department of Statistics and Econometrics, Laboratory of Data Science and Modeling, Faculty of Economics and Business Administration, Sofia University "St. Kl. Ohridski", 1113 Sofia, Bulgaria
Interests: modeling

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Co-Guest Editor
Associate Professor, Faculty of Economic and Social science, University of Plovdiv "Paisii Hilendarski", Plovdiv, Bulgaria
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Special Issue Information

Dear Colleagues,

We herewith present a Special Issue entitled “Big Data Analytics, Algorithms and Programming”. Big data has had an impressive impact on research and education in the many applied sciences. The fields in which big data is in development are computer science, statistics, and applied mathematics. Algorithms in big data analytics investigate large amounts of data to uncover hidden patterns, dependencies and other insights. Through modern technologies and innovations it is possible to analyse every kind of big data and get answers from it almost immediately in comparison with traditional business intelligence solutions. We are looking for significant scientific papers combining innovative programming approaches and algorithmic solutions in the field of big data analytics. Our focus areas include but are not limited to: machine learning algorithms, predictive analytics algorithms, time series forecasting algorithms, and applications of data analytics algorithms.

Prof. Dr. Ivan Ganchev Ivanov
Dr. Stanimir Kabaivanov
Guest Editor

Manuscript Submission Information

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Keywords

  • Big Data
  • Machine Learning Algorithms
  • Predictive Analytics Algorithms
  • Time Series Forecasting Algorithms
  • Data Analytics Algorithms

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Published Papers (1 paper)

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Research

17 pages, 6851 KiB  
Article
High-Precision Combined Tidal Forecasting Model
by Jiao Liu, Guoyou Shi and Kaige Zhu
Algorithms 2019, 12(3), 65; https://doi.org/10.3390/a12030065 - 26 Mar 2019
Cited by 12 | Viewed by 5673
Abstract
To improve the overall accuracy of tidal forecasting and ameliorate the low accuracy of single harmonic analysis, this paper proposes a combined tidal forecasting model based on harmonic analysis and autoregressive integrated moving average–support vector regression (ARIMA-SVR). In tidal analysis, the resultant tide [...] Read more.
To improve the overall accuracy of tidal forecasting and ameliorate the low accuracy of single harmonic analysis, this paper proposes a combined tidal forecasting model based on harmonic analysis and autoregressive integrated moving average–support vector regression (ARIMA-SVR). In tidal analysis, the resultant tide can be considered as a superposition of the astronomical tide level and the non-astronomical tidal level, which are affected by the tide-generating force and environmental factors, respectively. The tidal data are de-noised via wavelet analysis, and the astronomical tide level is subsequently calculated via harmonic analysis. The residual sequence generated via harmonic analysis is used as the sample dataset of the non-astronomical tidal level, and the tidal height of the system is calculated by the ARIMA-SVR model. Finally, the tidal values are predicted by linearly summing the calculated results of both systems. The simulation results were validated against the measured tidal data at the tidal station of Bay Waveland Yacht Club, USA. By considering the residual non-astronomical tide level effects (which are ignored in traditional harmonic analysis), the combined model improves the accuracy of tidal prediction. Moreover, the combined model is feasible and efficient. Full article
(This article belongs to the Special Issue Big Data Analytics, Algorithms and Programming)
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