Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
3.7
2.3
Journals
Algorithms
Special Issues
Big Data Analytics, Algorithms and Programming
share announcement

Big Data Analytics, Algorithms and Programming

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

Deadline for manuscript submissions: closed (15 September 2019) | Viewed by 5851

Special Issue Editors

Prof. Dr. Ivan Ganchev Ivanov

E-Mail Website
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
Dr. Stanimir Kabaivanov

E-Mail Website
Co-Guest Editor
Associate Professor, Faculty of Economic and Social science, University of Plovdiv "Paisii Hilendarski", Plovdiv, Bulgaria
Special Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

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 5239
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop